Search

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in –omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the increased cost due to the use of labeling reagents. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile organic solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50 μg dry TMT per channel effectively labels 6-12 μg peptides, yielding efficient TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. This streamlined workflow decreases reagent loss and reduces inter-sample variations. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for streamlined and robust quantitative proteomics and metaproteomics in high throughput applications.

The studies of microbial communities have drawn increased attention in various research fields such as agriculture, environment, and human health. Recently, metaproteomics has become a powerful tool to interpret the roles of the community members by investigating the expressed proteins of the microbes. However, analyzing the metaproteomic data sets at genome resolution is still challenging because of the lack of efficient bioinformatics tools. Here we develop MetaLab-MAG, a specially designed tool for the characterization of microbiomes from metagenome-assembled genomes databases. MetaLab-MAG was evaluated by analyzing various human gut microbiota data sets and performed comparably or better than searching the gene catalog protein database directly. MetaLab-MAG can quantify the genome-level microbiota compositions and supports both label-free and isobaric labeling-based quantification strategies. MetaLab-MAG removes the obstacles of metaproteomic data analysis and provides the researchers with in-depth and comprehensive information from the microbiomes.

The human gut microbiome is a complex system composed of hundreds of species, and metaproteomics can be used to explore their expressed functions. However, many lower abundance species are not detected by current metaproteomic techniques and represent the dark field of metaproteomics. We do not know the minimal abundance of a bacterium in a microbiome(depth) that can be detected by shotgun metaproteomics. In this study, we spiked

Inflammatory bowel diseases (IBDs), including Crohn's disease (CD) and ulcerative colitis, are chronic diseases of the gastrointestinal tract, with an unknown etiology, that affect over 6.8 million people worldwide. To characterize disease pathogenesis, proteomic and bioinformatic analyses were performed on colon biopsies collected during diagnostic endoscopy from 119 treatment-naïve pediatric patients, including from 78 IBD patients and 41 non-IBD patients who served as controls. Due to the presence of noninflamed and/or inflamed regions in IBD patients, up to two biopsies were obtained from IBD patients as compared to a single noninflamed biopsy from non-IBD pediatric control patients. Additional biopsies were obtained and analyzed from 33 of the IBD patients after IBD-directed therapeutic intervention for comparison of pre- and post-treatment proteomes. SuperSILAC was utilized to perform quantitative analysis of homogenized tissues, which were processed by filter-aided sample preparation. Hierarchical clustering and principal component analyses revealed proteomic patterns that distinguished inflamed from noninflamed tissues independent of therapy. Gene ontology revealed that proteins downregulated in inflammation are associated with metabolism, whereas upregulated proteins contribute to protein processing. A comparison of pre- and post-treatment proteomes from CD patients identified over 100 proteins that are significantly different between patients who responded and those who did not respond to therapy, including creatine kinase B and basigin.

Our gut microbiome functions like an organ, having its own set of functions and roles which can be modulated by various types of xenobiotic and biotic components. High-throughput screening approaches that are established based on in vitro or ex vivo cell, tissue or organ models greatly accelerate drug discovery and our understanding of biological and pathological processes within these systems. There was a lack of a high-throughput compatible functional screening approach of the gut microbiome until we recently developed the RapidAIM (Rapid Assay of Individual Microbiome). RapidAIM combines an optimized culturing model, which maintains the taxonomic and functional profiles of the human gut microbiome in vitro, and a high-throughput metaproteomics workflow to gain deep functional insights into microbiome responses. This protocol describes the most recently optimized 2.0 version of RapidAIM, consisting of extensive details on stool sample collection, biobanking, in vitro culturing and stimulation, microbiome sample processing, and metaproteomics measurement and data analysis. To demonstrate the typical outcome of the protocol, we show an example of using RapidAIM 2.0 to evaluate the effect of prebiotic kestose on ex vivo individual human gut microbiomes biobanked with five different workflows; we also show that kestose had consistent functional effects across individuals and can be used as positive control in the assay.

Graphical abstract, Resistant starches (RS) are dietary compounds processed by the gut microbiota into metabolites, such as butyrate, that are beneficial to the host. The production of butyrate by the microbiome appears to be affected by the plant source and type of RS as well as the individual’s microbiota. In this study, we used in vitro culture and metaproteomic methods to explore individual microbiome's functional responses to RS2 (enzymatically-resistant starch), RS3 (retrograded starch) and RS4 (chemically-modified starch). Results showed that RS2 and RS3 significantly altered the protein expressions in the individual gut microbiomes, while RS4 did not result in significant protein changes. Significantly elevated protein groups were enriched in carbohydrate metabolism and transport functions of families Eubacteriaceae, Lachnospiraceae and Ruminococcaceae. In addition, Bifidobacteriaceae was significantly increased in response to RS3. We also observed taxon-specific enrichments of starch metabolism and pentose phosphate pathways corresponding to this family. Functions related to starch utilization, ABC transporters and pyruvate metabolism pathways were consistently increased in the individual microbiomes in response to RS2 and RS3. Given that these taxon-specific responses depended on the type of carbohydrate sources, we constructed a functional ecological network to gain a system-level insight of functional organization. Our results suggest that while some microbes tend to be functionally independent, there are subsets of microbes that are functionally co-regulated by environmental changes, potentially by alterations of trophic interactions.

Metaproteomics has been used in combination with in vitro gut microbiota models to study drug-microbiome interactions. However, it remains unexplored whether the metaproteomics profile of in vitro gut microbiota responds differently to a same stimulus added at different growth phases. In this study, we cultured a human gut microbiota in 96-deep well plates using a previously validated model. Metformin was added during the lag, log, and stationary phases. Microbiome samples, collected at different time points, were analyzed by optical density and function by metaproteomic. The in vitro gut microbiota growth curves, taxonomy, and functional responses were different depending whether metformin was added during the lag, log, or stationary phases. The addition of drugs at the log phase may lead to the greatest decline of bacterial growth. Metaproteomic analysis suggests that the strength of the metformin effect on the gut microbiome functional profile may be ranked as lag phase > log phase > stationary phase. Metformin added at the lag phase may result in a significantly reduced level of the Clostridiales order and an increased level of the Bacteroides genus, which is different from stimulations during the rest of the growth phases. Metformin may also result in alterations of several pathways, including energy production and conversion, lipid transport and metabolism, translation, ribosomal structure, and biogenesis. Our results indicate that the timing for drug stimulation should be considered when studying drug-microbiome interactions in vitro.

The gut microbiome composition and function are associated with health and diseases. Sweeteners are widely used food additives, although many studies using animal models have linked sweetener consumption to gut microbial changes and health issues. Whether sweeteners directly change the human gut microbiome functionality remains largely unknown. In this study, we systematically investigated the responses of five human gut microbiomes to 21 common sweeteners, using an approach combining high-throughput ex vivo microbiome culturing and metaproteomics to quantify functional changes in different taxa. Hierarchical clustering based on metaproteomic responses of individual microbiomes resulted in two clusters. The first cluster was composed of non-caloric artificial sweeteners (NAS) and two sugar alcohols with shorter carbon backbones (4-5 carbon atoms), and the second cluster was composed of sugar alcohols with longer carbon backbones. The metaproteomic functional responses of the second cluster were similar to the prebiotic fructooligosaccharides and kestose, indicating that these sugar alcohol-type sweeteners have potential prebiotic functions. This study provides a comprehensive evaluation of the direct effects of commonly used sweeteners on the functions of the human gut microbiome using a functional metaproteomics approach, improving our understanding of the roles of sweeteners on microbiome-associated human health and disease issues.

Crohn's disease (CD) is characterized by the chronic inflammation of the gastrointestinal tract. A dysbiotic microbiome and a defective immune system are linked to CD, where hydrogen sulfide (H

Glycosylation is one of the most important post-translational modifications in biological systems. Current glycoproteome methods mainly focus on qualitative identification of glycosylation sites or intact glycopeptides. However, the systematic quantitation of glycoproteins has remained largely unexplored. Here, we developed a chemoenzymatic method to quantitatively investigate N-glycoproteome based on the N-glycan types. Taking advantage of the specificity of different endoglycosidases and isotope dimethyl labeling, six N-glycan types of structures linked on each glycopeptide, including high-mannose/hybrid, biantennary, and triantennary with/without core fucose, were quantified. As a proof of principle, the glycoproteomic N-glycan type quantitative (glyco-TQ) method was first used to determine the N-glycan type composition of the immunoglobulin G1 (IgG1) Fc fragment. Then we applied the method to analyze the glycan type profile of proteins from the breast cancer cell line MCF7, and we quantitatively revealed the N-glycan type microheterogeneity at the glycopeptide and glycoprotein level. The novel quantitative strategy to evaluate the relative intensity of the six states of N-glycan type glycosylation on each site provides a new avenue to investigate the function of glycoproteins in broad areas, such as cancer biomarker research, pharmaceuticals characterization, and antiglycan vaccine development.

COMPlex ASsociating with SET1 (COMPASS) is a histone H3 Lys-4 methyltransferase that typically marks the promoter region of actively transcribed genes. COMPASS is a multi-subunit complex in which the catalytic unit, SET1, is required for H3K4 methylation. An important subunit known to regulate SET1 methyltransferase activity is the CxxC zinc finger protein 1 (Cfp1). Cfp1 binds to COMPASS and is critical to maintain high level of H3K4me3 in cells but the mechanisms underlying its stimulatory activity is poorly understood. In this study, we show that Cfp1 only modestly activates COMPASS methyltransferase activity in vitro. Binding of Cfp1 to COMPASS is in part mediated by a new type of monovalent zinc finger (ZnF). This ZnF interacts with the COMPASS’s subunits RbBP5 and disruption of this interaction blunts its methyltransferase activity in cells and in vivo. Collectively, our studies reveal that a novel form of ZnF on Cfp1 enables its integration into COMPASS and contributes to epigenetic signaling.

Sleep disturbances and memory impairment are common symptoms of Alzheimer's disease (AD). Given that the circadian clock regulates sleep, hippocampal function, and neurodegeneration, it represents a therapeutic target against AD. Casein kinase 1δ/ε (CK1δ/ε) are clock regulators and overexpressed in AD brains, making them viable targets to improve sleep and cognition. In this study, we evaluated the therapeutic potential of a small molecule CK1δ/ε inhibitor (PF-670462) in a triple transgenic mouse model of AD (3xTg-AD). Mass spectrometry-based proteomic analyses revealed that PF-670462 administration in 3xTg-AD mice reversed hippocampal proteomic alterations in several AD-related and clock-regulated pathways, including synaptic plasticity and amyloid precursor protein processing. Furthermore, PF-670462 administration rescued working memory deficits and normalized behavioral circadian rhythm disturbances in 3xTg-AD mice. Our study provides in vivo proof of concept for CK1δ/ε inhibition against AD-associated hippocampal proteomic changes, memory impairment, and circadian disturbances.

Pediatric ulcerative colitis (UC) is a distinct type of inflammatory bowel disease with severe disease activity and rapid progression, which can lead to detrimental life-long consequences. The pathogenesis of pediatric UC remains unclear, although dysbiosis of the gut microbiota has been considered an important factor. In this study, we collected intestinal mucosal-luminal interface microbiota samples from a cohort of treatment-naïve pediatric UC or control patients and used two different mass spectrometry-based glycomic approaches to examine the N-glycans that were associated with the microbiota. We observed abundant small N-glycans that were associated with the microbiota and found that the pediatric UC microbiota samples contained significantly higher levels of these atypical N-glycans compared to those of controls. Four paucimannosidic or other truncated N-glycans were identified to successfully segregate UC from control patients with an area under the ROC curve of ≥0.9. This study indicates that the aberrant metabolism of glycans in the intestinal by gut microbiota may be involved in the pathogenesis of UC and intestinal N-glycans, including small glycans, can act as novel biomarker candidates for pediatric UC. SIGNIFICANCE: There is no cure for pediatric ulcerative colitis (UC) due to its unclear pathogenesis and the diagnosis of UC in children still largely depends on invasive colonoscopic examination. Recent evidence suggests that the dysbiosis of intestinal microbiota is associated with the onset and development of UC, however how the microbiota interact with the host remains unclear. This study used two different mass spectrometry-based glycomic approaches to quantitatively examine N-glycans that are associated with colonic mucosal-luminal interface microbiota of pediatric UC or control patients. To the best of our knowledge, this is the first comprehensive glycomic study of intestinal microbiota samples in UC, which demonstrated that intestinal microbiota was associated with abundant atypical small N-glycans with elevated levels in UC than controls. This study also identified four intestinal paucimannosidic or other truncated N-glycans as promising biomarker candidates for pediatric UC. These findings shed light on the mechanism study of host-microbiome interactions in UC and indicate that atypical glycans present in the gut can be a source for UC biomarker discovery.

BackgroundLysine acylations are important post-translational modifications that are present in both eukaryotes and prokaryotes, regulating diverse cellular functions. Our knowledge of the microbiome lysine acylation remains limited due to the lack of efficient analytical and bioinformatics methods for complex microbial communities.ResultsWe show that serial enrichment using motif antibodies successfully captures peptides containing lysine acetylation, propionylation and succinylation from human gut microbiome samples. A new bioinformatic workflow consisting of unrestricted database search confidently identified >60,000 acetylated, and ~20,000 propionylated and succinylated gut microbial peptides. Characterization of these identified modification-specific metaproteomes, i.e. meta-PTMomes, demonstrates that lysine acylations are differentially distributed in microbial species with different metabolic capabilities.ConclusionThis study provides an analytical framework, consisting of a serial immunoaffinity enrichment and an open database search strategy, for the study of lysine acylations in microbiome, which enables functional microbiome studies at the post-translational level.

SummaryMetaproteomics is used to explore the composition, dynamics and function of microbial communities. How-ever, acquiring data by tandem mass spectrometry is time consuming and resource intensive. To mediate this challenge, we present MetaProClust-MS1, a computational framework for microbiome screening developed to reduce the time required for data acquisition by mass spectrometry. In this proof-of-concept study, we tested MetaProClust-MS1 on data acquired using short 15 minute MS1-only mass spectrometry gradients and compared the results to those produced using data acquired by a traditional tandem mass spectrometry approach. MetaProClust-MS1 identified robust microbiome shifts caused by xenobiotics in both datasets. Cluster topologies were also significantly correlated. We demonstrate that MetaProClust-MS1 is able to rapidly screen microbiomes using only short MS1 profiles. This approach can be used to prioritize samples for deep metaproteomic analysis and will be especially useful in large-scale metaproteomic screens or in clinical settings where rapid results are required.

The gut microbiome and its metabolic processes are dynamic systems. Surprisingly, our understanding of gut microbiome dynamics is limited. Here, we report a metaproteomic workflow that involves protein stable isotope probing (protein-SIP) and identification/quantification of partially labeled peptides. We also developed a package, which we call MetaProfiler, that corrects for false identifications and performs phylogenetic and time series analysis for the study of microbiome dynamics. From the stool sample of five mice that were fed with 15N hydrolysate from Ralstonia eutropha, we identified 12 326 nonredundant unlabeled peptides, of which 8256 of their heavy counterparts were quantified. These peptides revealed incorporation profiles over time that were different between and within taxa, as well as between and within clusters of orthologous groups (COGs). Our study helps unravel the complex dynamics of protein synthesis and bacterial dynamics in the mouse microbiome. MetaProfiler and the bioinformatic pipeline are available at https://github.com/northomics/MetaProfiler.git.

Mass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS’ efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples.

Studying the structure and function of microbiomes is an emerging research field. Metaproteomic approaches focusing on the characterization of expressed proteins and post-translational modifications (PTMs) provide a deeper understanding of microbial communities. Previous research has highlighted the value of examining microbiome-wide protein expression in studying the roles of the microbiome in human diseases. Nevertheless, the regulation of protein functions in complex microbiomes remains underexplored. This is mainly due to the lack of efficient bioinformatics tools to identify and quantify PTMs in the microbiome. We have developed comprehensive software termed MetaLab for the data analysis of metaproteomic data sets. Here, we build an open search workflow within MetaLab for unbiased identification and quantification of unmodified peptides as well as peptides with various PTMs from microbiome samples. This bioinformatics platform provides information about proteins, PTMs, taxa, functions, and pathways of microbial communities. The performance of the workflow was evaluated using conventional proteomics, metaproteomics from mouse and human gut microbiomes, and modification-specific enriched data sets. Superior accuracy and sensitivity were obtained simultaneously by using our method compared with the traditional closed search strategy.

The understanding of the effects of compounds on the gut microbiome is limited. In particular, it is unclear whether structurally similar compounds would have similar or distinct effects on the gut microbiome. Here, we selected berberine (BBR), an isoquinoline quaternary alkaloid, and 16 structural analogs and evaluated their effects on seven individual gut microbiomes cultured in vitro. The responses of the individual microbiomes were evaluated by metaproteomic profiles and by assessing butyrate production. We show that both interindividual differences and compound treatments significantly contributed to the variance of metaproteomic profiles. BBR and eight analogs led to changes in proteins involved in microbial defense and stress responses and enrichment of proteins from Verrucomicrobia, Proteobacteria, and Bacteroidetes phyla. It also led to a decrease in proteins from the Firmicutes phylum and its Clostridiales order which correlated to decrease proteins involved in the butyrate production pathway and butyrate concentration. Three of the compounds, sanguinarine, chelerythrine, and ethoxysanguinarine, activated bacterial protective mechanisms, enriched Proteobacteria, increased opacity proteins, and markedly reduced butyrate production. Dihydroberberine had a similar function to BBR in enriching the Akkermansia genus. In addition, it showed less overall adverse impacts on the functionality of the gut microbiome, including a better maintenance of the butyrate level. Our study shows that ex vivo microbiome assay can assess differential regulating effects of compounds with subtle differences and reveals that compound analogs can have distinct effects on the microbiome.

Resistant starches (RS) are dietary compounds processed by the gut microbiota into metabolites, such as butyrate, that are beneficial to the host. The production of butyrate by the microbiome appears to be affected by the plant source and type of RS as well as the individual’s microbiota. In this study, we usedin vitroculture and metaproteomic methods to explore the consistency and variations in individual microbiome’s functional responses to three types of RS - RS2(Hi Maize 260), RS3(Novelose 330) and RS4(Fibersym RW). Results showed that RS2 and RS3 significantly altered the levels of protein expression in the individual gut microbiomes, while RS4 did not result in significant protein changes. Significantly elevated protein groups were enriched in carbohydrate metabolism and transport functions of families Eubacteriaceae, Lachnospiraceae and Ruminococcaceae. In addition, Bifidobacteriaceae was significantly increased in response to RS3. We also observed taxon-specific enrichments of starch metabolism and pentose phosphate pathways corresponding to this family. Functions related to starch utilization, ABC transporters and pyruvate metabolism pathways were consistently increased in the individual microbiomes in response to RS2 and RS3; in contrast, the downstream butyrate producing pathway response varied. Our study confirm that different types of RS have markedly variable functional effects on the human gut microbiome, and also found considerable inter-individual differences in microbiome pathway responses.

Background Human-targeted drugs may exert off-target effects or can be repurposed to modulate the gut microbiota. However, our understanding of such effects is limited due to a lack of rapid and scalable assay to comprehensively assess microbiome responses to drugs. Drugs and other compounds can drastically change the overall abundance, taxonomic composition, and functions of a gut microbiome. Results Here, we developed an approach to screen compounds against individual microbiomes in vitro, using metaproteomics to both measure absolute bacterial abundances and to functionally profile the microbiome. Our approach was evaluated by testing 43 compounds (including 4 antibiotics) against 5 individual microbiomes. The method generated technically highly reproducible readouts, including changes of overall microbiome abundance, microbiome composition, and functional pathways. Results show that besides the antibiotics, the compounds berberine and ibuprofen inhibited the accumulation of biomass during in vitro growth of the microbiota. By comparing genus and species level-biomass contributions, selective antibacterial-like activities were found with 35 of the 39 non-antibiotic compounds. Seven of the compounds led to a global alteration of the metaproteome, with apparent compound-specific patterns of functional responses. The taxonomic distributions of altered proteins varied among drugs, i.e., different drugs affect functions of different members of the microbiome. We also showed that bacterial function can shift in response to drugs without a change in the abundance of the bacteria. Conclusions Current drug-microbiome interaction studies largely focus on relative microbiome composition and microbial drug metabolism. In contrast, our workflow enables multiple insights into microbiome absolute abundance and functional responses to drugs. The workflow is robust, reproducible, and quantitative and is scalable for personalized high-throughput drug screening applications.

The surface of the human body, including the gastrointestinal tract, is occupied by trillions of microbial cells with diverse metabolic functions, constituting the human microbiome. The composition of the microbiome varies with body region and among individuals, and the majority of the microbiomes, their genes and protein products are poorly characterized. Recent studies have highlighted the importance of mass spectrometry-based omics approaches, and in particular metaproteomics, for deep functional characterization of the human microbiome. However, major analytical and bioinformatics challenges remain due to the enormous complexity, high dynamic range and large intra- and inter-individual variations in the human microbiome. Herein, we review the analytical approaches that have been implemented for metaproteomic studies of the microbiome and discuss the challenges in sample separation and instrument measurements. As well, we review the bioinformatics tools available for metaproteomic data processing, their challenges and their applications for deep characterizations of human microbiomes.

Elevated circulating cholesterol-rich low-density lipoprotein (LDL) particles increase coronary artery disease risk. Cell-surface hepatic LDL receptors (LDLRs) clear 70% of these particles from circulation. The ectodomain of LDLR is shed into circulation, preventing it from removing LDL particles. The role that LDLR ectodomain shedding plays as a regulatory mechanism is unknown.We describe LDLR shedding via the relationships between circulating soluble LDLRs (sLDLRs) and serum lipoproteins, serum proprotein convertase subtilin/kexin type 9 (PCSK9; a negative regulator of LDLR), and clinical parameters in a white Canadian population.Population-based, cross-sectional study.Clinical Research Center, The Ottawa Hospital, and Faculty of Medicine, University of Ottawa.Two hundred seventy-three white Canadians.None.sLDLR measured by ELISA; serum lipids and PCSK9, PCSK9 genotypes, and clinical parameters from previous analyses.sLDLRs correlated strongly with triglycerides (TG; r = 0.624, P0.0001) and moderately with LDL cholesterol (r = 0.384, P0.0001), and high-density lipoprotein cholesterol (r = -0.307, P = 0.0003). Only TG correlations were unaffected by PCSK9 variations. sLDLR levels were significantly elevated in those with TG50th or LDL cholesterol75th percentiles.Serum sLDLR levels correlate with several lipoprotein parameters, especially TG, and the presence of PCSK9 loss-of-function variants alters sLDLR levels and correlations, except for TG. Ectodomain LDLR shedding has a role in LDL metabolism, distinct from PCSK9, with interplay between these two pathways that regulate cell-surface LDLRs. Findings suggest alteration of LDLR shedding could emerge as a target to treat dyslipidemia.

Metaproteomics is a promising methodology for the functional characterizations of the gut microbiome. However, the performance of metaproteomic analysis is affected by protein extraction protocols in terms of the amount of protein recovered and the relative abundance of different bacteria observed in microbiome. Currently, there is a lack of consistency on protein extraction methods in published metaproteomics studies. Here we evaluated the effects of different protein extraction methods on human fecal metaproteome characterizations. We found that sodium dodecyl sulfate (SDS)-based lysis buffer obtained higher protein yields and peptide/protein group identifications compared to urea and the non-ionic detergent-based B-Per buffer. The addition of bead beating to any of the extraction buffers increased both protein yields and protein identifications. As well, bead beating led to a significant increase of the relative abundances of Firmicutes and Actinobacteria. We also demonstrated that ultrasonication, another commonly used mechanical disruption approach, performed even better than bead beating for gut microbial protein extractions. Importantly, proteins of the basic metabolic pathways showed significantly higher relative abundances when using ultrasonication. Overall, these results demonstrate that protein extraction protocols markedly impact the metaproteomic results and recommend a protein extraction protocol with both SDS and ultrasonication for metaproteomic studies. Biological significance The gut microbiome is emerging as an important factor influencing human health. Metaproteomics is promising for advancing the understanding of the functional roles of the microbiome in disease. However, metaproteomics suffers from a lack of consistent sample preparation procedures. In the present study, protein extraction protocols for fecal microbiome samples were evaluated for their effects on protein yields, peptide identifications, protein group identifications, taxonomic compositions and functional category distributions. While different protocols favor different microbial taxa and protein functions, our results suggest that a protein extraction protocol using sodium dodecyl sulfate (SDS) and ultrasonication provides the best performance for general shotgun metaproteomics studies.

Adverse tissue reactions to metal implants, including pseudotumors, can compromise implant functionality and survivorship. The identification of specific proteins in the synovial fluid (SF) of hip arthroplasty patients with a pseudotumor may lead to a better understanding of the underlying pathomechanisms. The objective of the present study was to compare the protein content of SF from patients with a short-term metal-on-metal hip implant associated with a pseudotumor and patients with a long-term metal-on-polyethylene hip implant associated with periprosthetic osteolysis. Discovery proteomics was used to identify differentially abundant proteins in albumin-depleted SF. In toto, 452 distinct proteins (present in at least half of the patients in one or both groups) were identified. Thirty of these 452 proteins were differentially abundant between the two groups, including two potential biomarkers: 6-phosphogluconate dehydrogenase (which plays a major protective role against oxidative stress) for the pseudotumor group, and scavenger receptor cysteine-rich type 1 protein M130 (which is involved in low-grade inflammation) for the periprosthetic osteolysis group. Other differentially abundant proteins identified suggest the presence of an adaptive immune response (particularly a type-IV hypersensitivity reaction), necrosis, and greater oxidative stress in patients with a pseudotumor. They also suggest the presence of an innate immune response, oxidative stress, tissue remodeling, and apoptosis in both patient groups, although differences in the specific proteins identified in each group point to differences in the pathomechanisms. Overall, results provide insights into the molecular mechanisms underlying metal-related pseudotumors and periprosthetic osteolysis, and may ultimately help elucidate pseudotumor etiology and assess the risk that asymptomatic pseudotumors will develop into an aggressive lesion. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1849-1859, 2018.

In vitro culture based approaches are time- and cost-effective solutions for rapidly evaluating the effects of drugs or natural compounds against microbiomes. The nutritional composition of the culture medium is an important determinant for effectively maintaining the gut microbiome in vitro. This study combines orthogonal experimental design and a metaproteomics approach to obtaining functional insights into the effects of different medium components on the microbiome. Our results show that the metaproteomic profile respond differently to medium components, including inorganic salts, bile salts, mucin, and short-chain fatty acids. Multifactor analysis of variance further revealed significant main and interaction effects of inorganic salts, bile salts, and mucin on the different functional groups of gut microbial proteins. While a broad regulating effect was observed on basic metabolic pathways, different medium components also showed significant modulations on cell wall, membrane, and envelope biogenesis and cell motility related functions. In particular, flagellar assembly related proteins were significantly responsive to the presence of mucin. This study provides information on the functional influences of medium components on the in vitro growth of microbiome communities and gives insight on the key components that must be considered when selecting and optimizing media for culturing ex vivo microbiotas.

Host-microbiome interactions have been shown to play important roles in human health and diseases. Most of the current studies of the microbiome have been performed by genomic approaches through next-generation sequencing. Technologies, such as metaproteomics, for functional analysis of the microbiome are needed to better understand the intricate host-microbiome interactions. However, significant efforts to improve the depth and resolution of gut metaproteomics are still required. In this study, we combined an efficient sample preparation technique, high resolution mass spectrometry, and metaproteomic bioinformatics tools to perform ultradeep metaproteomic analysis of human gut microbiome from stool. We reported the deepest analysis of the microbiome to date with an average of 20 558 protein groups identified per sample analysis. Moreover, strain resolution taxonomic and pathway analysis using deep metaproteomics revealed strain level variations, in particular for Faecalibacterium prausnitzii, in the microbiome from the different individuals. We also reported that the human proteins identified in stool samples are functionally enriched in extracellular region pathways and in particular those proteins involved in defense response against microbial organisms. Deep metaproteomics is a promising approach to perform in-depth microbiome analysis and simultaneously reveals both human and microbial changes that are not readily apparent using the standard genomic approaches.