Microbial Proteomics Research Articles

Bioinformatic Analysis to Investigate Metaproteome Composition Using Trans-Proteomic Pipeline.

With evidence emerging that the microbiome has a role in the onset of many human diseases, including cancer, analyzing these microbial communities and their proteins (i.e., the metaproteome) has become a powerful research tool. The Trans-Proteomic Pipeline (TPP) is a free, comprehensive software suite that facilitates the analysis of mass spectrometry (MS) data. By utilizing available microbial proteomes, TPP can identify microbial proteins and species, with an acceptable peptide false-discovery rate (FDR). An application to a publicly available oral cancer dataset is presented as an example to identify the viral metaproteome on the oral cancer invasive tumor front. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Collection of data and resources Basic Protocol 2: Analysis of MS data using TPP Basic Protocol 3: Analysis of TPP output using R in RStudio.

Chemical Links Between Redox Conditions and Estimated Community Proteomes from 16S rRNA and Reference Protein Sequences.

Environmental influences on community structure are often assessed through multivariate analyses in order to relate microbial abundances to separately measured physicochemical variables. However, genes and proteins are themselves chemical entities; in combination with genome databases, differences in microbial abundances directly encode for chemical variability. We predicted that the carbon oxidation state of estimated community proteomes, obtained by combining taxonomic abundances from published 16S rRNA gene sequencing datasets with reference microbial proteomes from the NCBI Reference Sequence (RefSeq) database, would reflect environmental oxidation-reduction conditions. Analysis of multiple datasets confirms the geobiochemical predictions for environmental redox gradients in hydrothermal systems, stratified lakes and marine environments, and shale gas wells. The geobiochemical signal is largest for the steep redox gradients associated with hydrothermal systems and between injected water and produced fluids from shale gas wells, demonstrating that microbial community composition can be a chemical proxy for environmental redox gradients. Although estimates of oxidation state from 16S amplicon and metagenomic sequences are correlated, the 16S-based estimates show stronger associations with redox gradients in some environments.

Effect of Organophosphate Esters on Microbial Community and Proteomics in Constructed Wetlands and its Removal Mechanism

Effect of Organophosphate Esters on Microbial Community and Proteomics in Constructed Wetlands and its Removal Mechanism

Factors Influencing Proteolysis and Protein Utilization in the Intestine of Pigs: A Review.

Simple SummaryIn order to minimize protein requirements and nitrogen emissions in pig production, it is important to understand the physiology of protein breakdown in the pig’s gastrointestinal tract and to find ways to improve the protein utilization efficiency of the animals. In this review, we summarize and discuss factors influencing protein degradation and thereby affecting the efficiency of the animals. We shed light on the individual pillars of protein breakdown, starting with the enzymatic breakdown of the fed protein, via the amino acid transporters absorbing in the intestine, to the proteolytic capacity of the microbial and animal-specific proteome. The available literature shows the specific activities and expression levels of proteolytic enzymes and AA transporters depending on the availability of free or bound AA in the feed. Improvements in nutrient digestibility result from changes in nutrient transporter and enzyme gene expression, as well as a change in microbial activity. We are of the opinion that in the future, also with the help of transcriptomics, more time should be invested in researching the physiology of protein degradation and the associated protein utilization efficiency using highly standardized animal trials.Pigs are among the most important farm animals for meat production worldwide. In order to meet the amino acid requirements of the animals, pigs rely on the regular intake of proteins and amino acids with their feed. Unfortunately, pigs excrete about two thirds of the used protein, and production of pork is currently associated with a high emission of nitrogen compounds resulting in negative impacts on the environment. Thus, improving protein efficiency in pigs is a central aim to decrease the usage of protein carriers in feed and to lower nitrogen emissions. This is necessary as the supply of plant protein sources is limited by the yield and the cultivable acreage for protein plants. Strategies to increase protein efficiency that go beyond the known feeding options have to be investigated considering the characteristics of the individual animals. This requires a deep understanding of the intestinal processes including enzymatic activities, capacities of amino acid transporters and the microbiome. This review provides an overview of these physiological factors and the respective analyses methods.

DiagnoTop: A Computational Pipeline for Discriminating Bacterial Pathogens without Database Search.

Pathogen identification is crucial to confirm bacterial infections and guide antimicrobial therapy. Although MALDI-TOF mass spectrometry (MS) serves as foundation for tools that enable rapid microbial identification, some bacteria remain challenging to identify. We recently showed that top-down proteomics (TDP) could be used to discriminate closely related enterobacterial pathogens (Escherichia coli, Shigella, and Salmonella) that are indistinguishable with tools rooted in the MALDI-TOF MS approach. Current TDP diagnostic relies on the identification of specific proteoforms for each species through a database search. However, microbial proteomes are often poorly annotated, which complicates the large-scale identification of proteoforms and leads to many unidentified high-quality mass spectra. Here, we describe a new computational pipeline called DiagnoTop that lists discriminative spectral clusters found in TDP data sets that can be used for microbial diagnostics without database search. Applied to our enterobacterial TDP data sets, DiagnoTop could easily shortlist high-quality discriminative spectral clusters, leading to increased diagnostic power. This pipeline opens new perspectives in clinical microbiology and biomarker discovery using TDP.

Mesoporous Silica as Sorbents and Enzymatic Nanoreactors for Microbial Membrane Proteomics.

The membrane proteins of microbes are at the forefront of host and parasite interactions. Having a general view of the functions of microbial membrane proteins is vital for many biomedical studies on microbiota. Nevertheless, due to the strong hydrophobicity and low concentration of membrane proteins, it is hard to efficiently enrich and digest the proteins for mass spectrometry analysis. Herein, we design an enzymatic nanoreactor for the digestion of membrane proteins using methylated well-ordered hexagonal mesoporous silica (Met-SBA-15). The material can efficiently extract hydrophobic membrane proteins and host the proteolysis in nanopores. The performance of the enzymatic nanoreactor is first demonstrated using standard hydrophobic proteins and then validated using membrane proteins extracted from Escherichia coli (E. coli) or a mixed bacterial sample of eight strains. Using the nanoreactor, 431 membrane proteins are identified from E. coli, accounting for 38.5% of all membrane proteins of the species, which is much more than that by the widely used in-solution digestion protocol. From the mixed bacterial sample of eight strains, 1395 membrane proteins are identified using the nanoreactor. On the contrary, the traditional in-solution proteolysis workflow only leads to the identification of 477 membrane proteins, demonstrating that the Met-SBA-15 can be offered as an excellent tool for microbial membrane proteome research and is expected to be used in human microbiota studies, e.g. host-microbe interactions.

Bringing New Methods to the Seed Proteomics Platform: Challenges and Perspectives.

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins, and carbohydrates in their seeds, distinctly dominate modern agriculture, thus play an essential role in food industry and fuel production. Therefore, seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, seed development and germination as well as age- and stress-related alterations in seed vigor, longevity, nutritional value, and safety can be addressed by a broad panel of analytical, biochemical, and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes accompanying plant development, senescence, and response to biotic or abiotic stress. Among individual post-genomic methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. During the recent decades, multiple methodological advances were introduced in different branches of life science, although only some of them were established in seed proteomics so far. Therefore, here we discuss main methodological approaches already employed in seed proteomics, as well as those still waiting for implementation in this field of plant research, with a special emphasis on sample preparation, data acquisition, processing, and post-processing. Thereby, the overall goal of this review is to bring new methodologies emerging in different areas of proteomics research (clinical, food, ecological, microbial, and plant proteomics) to the broad society of seed biologists.

Bacterial Immunogenicity Prediction by Machine Learning Methods.

The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis, k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), random subspace method (RSM), and extreme gradient boosting) on a set of 317 known bacterial immunogens and 317 bacterial non-immunogens and derived models for immunogenicity prediction. The models were validated by internal cross-validation in 10 groups from the training set and by the external test set. All of them showed good predictive ability, but the xgboost model displays the most prominent ability to identify immunogens by recognizing 84% of the known immunogens in the test set. The combined RSM-kNN model was the best in the recognition of non-immunogens, identifying 92% of them in the test set. The three best performing ML models (xgboost, RSM-kNN, and RF) were implemented in the new version of the server VaxiJen, and the prediction of bacterial immunogens is now based on majority voting.

Construction of the microbial protein metabolism map of tea rhizosphere soil in acidified plantations

Soil plays a crucial role in tea tree growth, however, only few studies have reported the responses of soil microorganisms to soil acidification in tea plantations and the negative regulation of soil acidification on tea tree growth. In this study, the soil samples from Tieguanyin tea tree plantation with different acidities were used as a material for rhizosphere soil microbial protein extraction. Data dependent acquisition (DDA) was used to establish the soil microbial protein data, whereas Data independent acquisition (DIA) was used for quantitative analyses to obtain the reduced abundance of soil protein in tea rhizosphere soil. Furthermore, the functional analysis of the obtained proteins was conducted, and the regulation map of microbial protein metabolism of rhizosphere soil was constructed. The results showed that a total of 2,741 microbial proteins were detected in the tea tree rhizosphere soil, and the molecular weight ranged from 2.64 to 338.33 kD, whereas the range of pI was from 3.78 to 12.15. Quantitative analyses of soil proteins showed that the abundance of 54 microbial proteins, including genetic signaling proteins, energy metabolism signaling proteins, soil antioxidant signaling proteins, and soil nutrient cycling signaling proteins, which are named according to their functions, was decreased. Based on these analyses, a metabolic regulation map of soil microbial proteomes was constructed, and the results suggested that as tea soil acidity increased, soil microbes released quorum-sensing proteins to trigger signaling proteins for survival. Under the regulation of signaling pathway proteins, signaling proteins activated the expression of specific signaling proteins in different metabolic pathways and then regulated the expression of proteins in different metabolic pathways, demonstrating a downregulated expression trend. Finally, this regulation resulted in a decrease of microbial energy metabolism and circulation capacity as well as the reproduction capacity and quantity. The change in microbial quantity and function led to the weakening of soil nutrient cycling capacity, the blocking of the absorption of nutrients in tea trees, and the stunting of the growth and the declining of the yield and quality of tea trees.

Pregnancy associates with alterations to the host and microbial proteome in vaginal mucosa.

ProblemPregnant women are at increased risk of HIV acquisition, but the biological mechanisms contributing to this observation are not well understood.Method of StudyHere, we assessed host immune and microbiome differences in the vaginal mucosa of healthy pregnant and non‐pregnant women using a metaproteomics approach. Cervicovaginal lavage (CVL) samples were collected from 23 pregnant and 25 non‐pregnant women.ResultsMass spectrometry analysis of CVL identified 550 human proteins and 376 bacterial proteins from 11 genera. Host proteome analysis indicated 56 human proteins (10%) were differentially abundant (P < .05) between pregnant and non‐pregnant women, including proteins involved in angiogenesis (P = 3.36E‐3), cell movement of phagocytes (P = 1.34E‐6), and permeability of blood vessels (P = 1.27E‐4). The major bacterial genera identified were Lactobacillus, Gardnerella, Prevotella, Megasphaera, and Atopobium. Pregnant women had higher levels of Lactobacillus species (P = .017) compared with non‐pregnant women. Functional pathway analysis indicated that pregnancy associated with changes to bacterial metabolic pathway involved in energy metabolism, which were increased in pregnant women (P = .035).ConclusionOverall, pregnant women showed differences in the cervicovaginal proteome and microbiome that may be important for HIV infection risk.

Potential of proteomics to probe microbes.

An organism exposed to a plethora of environmental perturbations undergoes proteomic changes which enablethe characterization of total proteins in it. Much of the proteomic information is obtained from genomic data. Additional information on the proteome such as posttranslational modifications, protein-protein interactions, protein localization, metabolic pathways, and so on are deduced using proteomic tools which genomics and transcriptomics fail to offer. The proteomic analysis allows identification of precise changes in proteins, which in turn solve the complexity of microbial population providing insights into the microbial metabolism, cellular pathways, and behavior of microorganisms in new environments. Furthermore, they provide clues for the exploitation of their special features for biotechnological applications. Numerous techniques for the analysis of microbial proteome such as electrophoretic, chromatographic, mass spectrometric-based methods as well as quantitative proteomics are available which facilitate protein separation, expression, identification, and quantification of proteins. An understanding of the potential of each of the proteomic tools has created a significant impact on diverse microbiological aspects and the same has been discussed in this review.

The Role of the Canine Gut Microbiome and Metabolome in Health and Gastrointestinal Disease

The gut microbiome contributes to host metabolism, protects against pathogens, educates the immune system, and, through these basic functions, affects directly or indirectly most physiologic functions of its host. Molecular techniques have allowed us to expand our knowledge by unveiling a wide range of unculturable bacteria that were previously unknown. Most bacterial sequences identified in the canine gastrointestinal (GI) tract fall into five phyla: Firmicutes, Fusobacteria, Bacteroidetes, Proteobacteria, and Actinobacteria. While there are variations in the microbiome composition along the GI tract, most clinical studies concentrate on fecal microbiota. Age, diet, and many other environmental factors may play a significant role in the maintenance of a healthy microbiome, however, the alterations they cause pale in comparison with the alterations found in diseased animals. GI dysfunctions are the most obvious association with gut dysbiosis. In dogs, intestinal inflammation, whether chronic or acute, is associated with significant differences in the composition of the intestinal microbiota. Gut dysbiosis happens when such alterations result in functional changes in the microbial transcriptome, proteome, or metabolome. Commonly affected metabolites include short-chain fatty acids, and amino acids, including tryptophan and its catabolites. A recently developed PCR-based algorithm termed “Dysbiosis Index” is a tool that allows veterinarians to quantify gut dysbiosis and can be used to monitor disease progression and response to treatment. Alterations or imbalances in the microbiota affect immune function, and strategies to manipulate the gut microbiome may be useful for GI related diseases. Antibiotic usage induces a rapid and significant drop in taxonomic richness, diversity, and evenness. For that reason, a renewed interest has been put on probiotics, prebiotics, and fecal microbiota transplantation (FMT). Although probiotics are typically unable to colonize the gut, the metabolites they produce during their transit through the GI tract can ameliorate clinical signs and modify microbiome composition. Another interesting development is FMT, which may be a promising tool to aid recovery from dysbiosis, but further studies are needed to evaluate its potential and limitations.

Shot-gun proteomics: why thousands of unidentified signals matter.

Mass spectrometry-based proteomics has become a constitutional part of the multi-omics toolbox in yeast research, advancing fundamental knowledge of molecular processes and guiding decisions in strain and product developmental pipelines. Nevertheless, post-translational protein modifications (PTMs) continue to challenge the field of proteomics. PTMs are not directly encoded in the genome; therefore, they require a sensitive analysis of the proteome itself. In yeast, the relevance of post-translational regulators has already been established, such as for phosphorylation, which can directly affect the reaction rates of metabolic enzymes. Whereas, the selective analysis of single modifications has become a broadly employed technique, the sensitive analysis of a comprehensive set of modifications still remains a challenge. At the same time, a large number of fragmentation spectra in a typical shot-gun proteomics experiment remain unidentified. It has been estimated that a good proportion of those unidentified spectra originates from unexpected modifications or natural peptide variants. In this review, recent advancements in microbial proteomics for unrestricted protein modification discovery are reviewed, and recent research integrating this additional layer of information to elucidate protein interaction and regulation in yeast is briefly discussed.

Associations of the Fecal Microbial Proteome Composition and Proneness to Diet-induced Obesity

Consumption of refined high-fat, low-fiber diets promotes development of obesity and its associated consequences. Although genetics play an important role in dictating susceptibility to such obesogenic diets, mice with nearly uniform genetics exhibit marked heterogeneity in their extent of obesity in response to such diets. This suggests non-genetic determinants play a role in diet-induced obesity. Hence, we sought to identify parameters that predict, and/or correlate with, development of obesity in response to an obesogenic diet. We assayed behavior, metabolic parameters, inflammatory markers/cytokines, microbiota composition, and the fecal metaproteome, in a cohort of mice (n = 50) prior to, and the 8 weeks following, administration of an obesogenic high-fat low-fiber diet. Neither behavioral testing nor quantitation of inflammatory markers broadly predicted severity of diet-induced obesity. Although, the small subset of mice that exhibited basal elevations in serum IL-6 (n = 5) were among the more obese mice in the cohort. While fecal microbiota composition changed markedly in response to the obesogenic diet, it lacked the ability to predict which mice were relative prone or resistant to obesity. In contrast, fecal metaproteome analysis revealed functional and taxonomic differences among the proteins associated with proneness to obesity. Targeted interrogation of microbiota composition data successfully validated the taxonomic differences seen in the metaproteome. Although future work will be needed to determine the breadth of applicability of these associations to other cohorts of animals and humans, this study nonetheless highlights the potential power of gut microbial proteins to predict and perhaps impact development of obesity.

The Farmed Atlantic Salmon (Salmo salar) Skin-Mucus Proteome and Its Nutrient Potential for the Resident Bacterial Community.

Norway is the largest producer and exporter of farmed Atlantic salmon (Salmo salar) worldwide. Skin disorders correlated with bacterial infections represent an important challenge for fish farmers due to the economic losses caused. Little is known about this topic, thus studying the skin–mucus of Salmo salar and its bacterial community depict a step forward in understanding fish welfare in aquaculture. In this study, we used label free quantitative mass spectrometry to investigate the skin–mucus proteins associated with both Atlantic salmon and bacteria. In particular, the microbial temporal proteome dynamics during nine days of mucus incubation with sterilized seawater was investigated, in order to evaluate their capacity to utilize mucus components for growth in this environment. At the start of the incubation period, the largest proportion of proteins (~99%) belonged to the salmon and many of these proteins were assigned to protecting functions, confirming the defensive role of mucus. On the contrary, after nine days of incubation, most of the proteins detected were assigned to bacteria, mainly to the genera Vibrio and Pseudoalteromonas. Most of the predicted secreted proteins were affiliated with transport and metabolic processes. In particular, a large abundance and variety of bacterial proteases were observed, highlighting the capacity of bacteria to degrade the skin–mucus proteins of Atlantic salmon.

Metaproteomics reveals persistent and phylum-redundant metabolic functional stability in adult human gut microbiomes of Crohn\u2019s remission patients despite temporal variations in microbial taxa, genomes, and proteomes

BackgroundThe gut microbiome plays a fundamental role in the human host’s overall health by contributing key biological functions such as expanded metabolism and pathogen defense/immune control. In a healthy individual, the gut microbiome co-exists within the human host in a symbiotic, non-inflammatory relationship that enables mutual benefits, such as microbial degradation of indigestible food products into small molecules that the host can utilize, and enhanced pathogen defense. In abnormal conditions, such as Crohn’s disease, this favorable metabolic relationship breaks down and a variety of undesirable activities result, including chronic inflammation and other health-related issues. It has been difficult, however, to elucidate the overall functional characteristics of this relationship because the microbiota can vary substantially in composition for healthy humans and possibly even more in individuals with gut disease conditions such as Crohn’s disease. Overall, this suggests that microbial membership composition may not be the best way to characterize a phenotype. Alternatively, it seems to be more informative to examine and characterize the functional composition of a gut microbiome. Towards that end, this study examines 25 metaproteomes measured in several Crohn’s disease patients’ post-resection surgery across the course of 1 year, in order to examine persistence of microbial taxa, genes, proteins, and metabolic functional distributions across time in individuals whose microbiome might be more variable due to the gut disease condition.ResultsThe measured metaproteomes were highly personalized, with all the temporally-related metaproteomes clustering most closely by individual. In general, the metaproteomes were remarkably distinct between individuals and to a lesser extent within individuals. This prompted a need to characterize the metaproteome at a higher functional level, which was achieved by annotating identified protein groups with KEGG orthologous groups to infer metabolic modules. At this level, similar and redundant metabolic functions across multiple phyla were observed across time and between individuals. Tracking through these various metabolic modules revealed a clear path from carbohydrate, lipid, and amino acid degradation to central metabolism and finally the production of fermentation products.ConclusionsThe human gut metaproteome can vary quite substantially across time and individuals. However, despite substantial intra-individual variation in the metaproteomes, there is a clear persistence of conserved metabolic functions across time and individuals. Additionally, the persistence of these core functions is redundant across multiple phyla but is not always observable in the same sample. Finally, the gut microbiome’s metabolism is not driven by a set of discrete linear pathways but a web of interconnected reactions facilitated by a network of enzymes that connect multiple molecules across multiple pathways.

A proteome-wide screen of Campylobacter jejuni using protein microarrays identifies novel and conformational antigens.

Campylobacter jejuni (C. jejuni) is a foodborne intestinal pathogen and major cause of gastroenteritis worldwide. C. jejuni proteins that are immunogenic have been sought for their potential use in the development of biomarkers, diagnostic assays, or subunit vaccines for humans or livestock. To identify new immunogenic C. jejuni proteins, we used a native protein microarray approach. A protein chip, with over 1400 individually purified GST-tagged C. jejuni proteins, representing over 86% of the proteome, was constructed to screen for antibody titers present in test sera raised against whole C. jejuni cells. Dual detection of GST signals was incorporated as a way of normalizing the variation of protein concentrations contributing to the antibody staining intensities. We detected strong signals to 102 C. jejuni antigens. In addition to antigens recognized by antiserum raised against C. jejuni, parallel experiments were conducted to identify antigens cross-reactive to antiserum raised against various serotypes of E. coli or Salmonella or to healthy human sera. This led to the identification of 34 antigens specifically recognized by the C. jejuni antiserum, only four of which were previously known. The chip approach also allowed identification of conformational antigens. We demonstrate in the case of Cj1621 that antigen signals are lost to denaturing conditions commonly used in other approaches to identify immunogens. Antigens identified in this study include those possessing sequence features indicative of cell surface localization, as well as those that do not. Together, our results indicate that the unbiased chip-based screen can help reveal the full repertoire of host antibodies against microbial proteomes.

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

Exploration of Nicotine Metabolism in Paenarthrobacter nicotinovorans pAO1 by Microbial Proteomics.

Proteomics, or the large-scale study of proteins, is a post-genomics field that, together with transcriptomics and metabolomics, has moved the study of bacteria to a new era based on system-wide understanding of bacterial metabolic and regulatory networks. The study of bacterial proteins or microbial proteomics has found a wide array of applications in many fields of microbiology, from food, clinical, and industrial microbiology to microbial ecology and physiology. The current chapter makes a brief technical introduction into the available approaches for the large-scale study of bacterial proteins using mass-spectrometry. Furthermore, the advantages and disadvantages of using bacteria for proteomics studies are indicated as well as several example studies where MS-based bacterial proteomics had a fundamental role in deciphering the scientific question. Finally, the proteomics study of nicotine catabolism in Paenarthrobacter nicotinovorans pAO1 using nanoLC-MS/MS is given as an in-depth example for possible applications of microbial proteomics.The nicotine degradation pathway functioning in Paenarthrobacter nicotinovorans is encoded by the catabolic megaplasmid pAO1 that contains about 40 nicotine-related genes making out the nic-gens cluster. Despite the promising biotechnological potential for the production of green-chemicals, only half of the nic-genes have been experimentally linked to nicotine. In an attempt to systematically identify all the proteins involved in nicotine degradation, a gel-based proteomics approach was used to identify a total of 801 proteins when Paenarthrobacter nicotinovorans was grown on three carbon sources: citrate, nicotine and nicotine and citrate. The differences in protein abundance showed that the bacterium is able to switch between deamination and demethylation in the lower nicotine pathway based on the available C source. Several pAO1 putative genes including a hypothetical polyketide cyclase have been shown to have a nicotine-dependent expression and we hypothesize that the polyketide cyclase would hydrolyze the N1-C6 bond from the pyridine ring with the formation of alpha-keto-glutaramate. Two chromosomal proteins, a malate dehydrogenase, and a D-3-phosphoglycerate dehydrogenase were shown to be strongly upregulated when nicotine was the sole carbon source and could be related to the production of the alpha-keto-glutaramate by the polyketide cyclase.

Hospitalized Premature Infants Are Colonized by Related Bacterial Strains with Distinct Proteomic Profiles.

ABSTRACTDuring the first weeks of life, microbial colonization of the gut impacts human immune system maturation and other developmental processes. In premature infants, aberrant colonization has been implicated in the onset of necrotizing enterocolitis (NEC), a life-threatening intestinal disease. To study the premature infant gut colonization process, genome-resolved metagenomics was conducted on 343 fecal samples collected during the first 3 months of life from 35 premature infants housed in a neonatal intensive care unit, 14 of whom developed NEC, and metaproteomic measurements were made on 87 samples. Microbial community composition and proteomic profiles remained relatively stable on the time scale of a week, but the proteome was more variable. Although genetically similar organisms colonized many infants, most infants were colonized by distinct strains with metabolic profiles that could be distinguished using metaproteomics. Microbiome composition correlated with infant, antibiotics administration, and NEC diagnosis. Communities were found to cluster into seven primary types, and community type switched within infants, sometimes multiple times. Interestingly, some communities sampled from the same infant at subsequent time points clustered with those of other infants. In some cases, switches preceded onset of NEC; however, no species or community type could account for NEC across the majority of infants. In addition to a correlation of protein abundances with organism replication rates, we found that organism proteomes correlated with overall community composition. Thus, this genome-resolved proteomics study demonstrated that the contributions of individual organisms to microbiome development depend on microbial community context.