Chongle Pan

Integrated metagenomics/metaproteomics reveals human host microbiota signatures of Crohn's disease

Crohns disease (CD) is an inflammatory bowel disease of complex etiology, although dysbiosis of the gut microbiota has been implicated in chronic immune-mediated inflammation associated with CD. Here we combined shotgun metagenomic and metaproteomic approaches to identify potential functional signatures of CD in stool samples from six twin pairs that were either healthy, or that had CD in the ileum (ICD) or colon (CCD). Integration of these omics approaches revealed several genes, proteins, and pathways that primarily differentiated ICD from healthy subjects, including depletion of many proteins in ICD. In addition, the ICD phenotype was associated with alterations in bacterial carbohydrate metabolism, bacterial-host interactions, as well as human host-secreted enzymes. This eco-systems biology approach underscores the link between the gut microbiota and functional alterations in the pathophysiology of Crohns disease and aids in identification of novel diagnostic targets and disease specific biomarkers.

Impact of pretreated Switchgrass and biomass carbohydrates on Clostridium thermocellum ATCC 27405 cellulosome composition: a quantitative proteomic analysis.

Background Economic feasibility and sustainability of lignocellulosic ethanol production requires the development of robust microorganisms that can efficiently degrade and convert plant biomass to ethanol. The anaerobic thermophilic bacterium Clostridium thermocellum is a candidate microorganism as it is capable of hydrolyzing cellulose and fermenting the hydrolysis products to ethanol and other metabolites. C. thermocellum achieves efficient cellulose hydrolysis using multiprotein extracellular enzymatic complexes, termed cellulosomes. Methodology/Principal Findings In this study, we used quantitative proteomics (multidimensional LC-MS/MS and 15N-metabolic labeling) to measure relative changes in levels of cellulosomal subunit proteins (per CipA scaffoldin basis) when C. thermocellum ATCC 27405 was grown on a variety of carbon sources [dilute-acid pretreated switchgrass, cellobiose, amorphous cellulose, crystalline cellulose (Avicel) and combinations of crystalline cellulose with pectin or xylan or both]. Cellulosome samples isolated from cultures grown on these carbon sources were compared to 15N labeled cellulosome samples isolated from crystalline cellulose-grown cultures. In total from all samples, proteomic analysis identified 59 dockerin- and 8 cohesin-module containing components, including 16 previously undetected cellulosomal subunits. Many cellulosomal components showed differential protein abundance in the presence of non-cellulose substrates in the growth medium. Cellulosome samples from amorphous cellulose, cellobiose and pretreated switchgrass-grown cultures displayed the most distinct differences in composition as compared to cellulosome samples from crystalline cellulose-grown cultures. While Glycoside Hydrolase Family 9 enzymes showed increased levels in the presence of crystalline cellulose, and pretreated switchgrass, in particular, GH5 enzymes showed increased levels in response to the presence of cellulose in general, amorphous or crystalline. Conclusions/Significance Overall, the quantitative results suggest a coordinated substrate-specific regulation of cellulosomal subunit composition in C. thermocellum to better suit the organisms needs for growth under different conditions. To date, this study provides the most comprehensive comparison of cellulosomal compositional changes in C. thermocellum in response to different carbon sources. Such studies are vital to engineering a strain that is best suited to grow on specific substrates of interest and provide the building blocks for constructing designer cellulosomes with tailored enzyme composition for industrial ethanol production.

Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos.

A variety of quantitative proteomics methods have been developed, including label-free, metabolic labeling, and isobaric chemical labeling using iTRAQ or TMT. Here, these methods were compared in terms of the depth of proteome coverage, quantification accuracy, precision, and reproducibility using a high-performance hybrid mass spectrometer, LTQ Orbitrap Velos. Our results show that (1) the spectral counting method provides the deepest proteome coverage for identification, but its quantification performance is worse than labeling-based approaches, especially the quantification reproducibility; (2) metabolic labeling and isobaric chemical labeling are capable of accurate, precise, and reproducible quantification and provide deep proteome coverage for quantification; isobaric chemical labeling surpasses metabolic labeling in terms of quantification precision and reproducibility; and (3) iTRAQ and TMT perform similarly in all aspects compared in the current study using a CID-HCD dual scan configuration. On the basis of the unique advantages of each method, we provide guidance for selection of the appropriate method for a quantitative proteomics study.

Effects of diet on resource utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome.

Artificial human gut microbial communities implanted into germ-free mice provide insights into how species-level responses to changes in diet give rise to community-level structural and functional reconfiguration and how types of bacteria prioritize use of available nutrients in vivo.

Metaproteomics: Harnessing the Power of High Performance Mass Spectrometry to Identify the Suite of Proteins That Control Metabolic Activities in Microbial Communities

The availability of extensive genome information for many different microbes, including unculturable species in mixed communities from environmental samples, has enabled systems-biology interrogation by providing a means to access genomic, transcriptomic, and proteomic information. To this end, metaproteomics exploits the power of high-performance mass spectrometry for extensive characterization of the complete suite of proteins expressed by a microbial community in an environmental sample.

Improved genome annotation for Zymomonas mobilis

893 respective quality scores and the details of the software and parameters used in study are available at our website (Supplementary Table 1). We have also sequenced the genome of an acetate-tolerant strain derived from Z. mobilis ZM4 ATCC31821 that was selected in another geographically separated laboratory7 and report 454 pyrosequencing and Sanger sequencing and peptide support for our changes to the ZM4 chromosome (Supplementary Table 1). In addition, the entire ZM4 pyrosequencing data set has been deposited in the National Center for Biotechnology Information (NCBI) shortread archive database (Study SRP000908). We processed the updated sequence data using the automated Oak Ridge National Laboratory (ORNL) microbial genome annotation pipeline. Finally, we examined the gene models predicted in the original GenBank annotation, the TIGR reannotation and our new reannotation and updated the ZM4 annotation in a final manual curation step. The final curation was performed in conjunction with a defined set of criteria (available with reannotation) and several proteomics data sets that showed peptide support for more than half of the theoretical proteome. An overview of the extensive changes made to the ZM4 chromosome based upon mass-spectrometry proteomics and pyrosequencing data and six illustrative examples are presented (Table 1 and Supplementary Fig. 1, respectively). We have converted 61 pseudogenes in the original annotation into 43 full-length coding sequences, which include predicted genes with important metabolic and physiological functions (e.g., GenBank acc. nos. for tRNA synthetases ZMO0460, ZMO0843, ZMO0845, ZMO1508, ZMO1878 and flagella gene fliF, ZMO0633) (Supplementary Table 2). Several of the updated chromosomal nucleotides are consistent with earlier ZM4 fosmid DNA sequence data (e.g., GenBank acc. no. AAG29859) and we have peptide support for 6 of our 37 newly predicted chromosomal genes (Supplementary Table 3). We did not identify peptides corresponding to any of the putative genes that we deleted. A comprehensive comparison on a gene-by-gene basis is presented in Supplementary Table 4. We have provided our analysis to the authors of the primary genome annotation and they are in the process of updating their GenBank submission. Plasmid DNA was also identified in our 454-pyrosequencing data, which was the financial sustainability of biomedical innovation in the private sector. This in turn will help secure the future of these areas against any further crises.

Quantitative Tracking of Isotope Flows in Proteomes of Microbial Communities

Stable isotope probing (SIP) has been used to track nutrient flows in microbial communities, but existing protein-based SIP methods capable of quantifying the degree of label incorporation into peptides and proteins have been demonstrated only by targeting usually less than 100 proteins per sample. Our method automatically (i) identifies the sequence of and (ii) quantifies the degree of heavy atom enrichment for thousands of proteins from microbial community proteome samples. These features make our method suitable for comparing isotopic differences between closely related protein sequences, and for detecting labeling patterns in low-abundance proteins or proteins derived from rare community members. The proteomic SIP method was validated using proteome samples of known stable isotope incorporation levels at 0.4%, ∼50%, and ∼98%. The method was then used to monitor incorporation of 15N into established and regrowing microbial biofilms. The results indicate organism-specific migration patterns from established communities into regrowing communities and provide insights into metabolism during biofilm formation. The proteomic SIP method can be extended to many systems to track fluxes of 13C or 15N in microbial communities.

A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry

BackgroundHigh-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate de novo sequencing for identification of post-translational modifications and amino acid polymorphisms.ResultsIn this study, a new de novo sequencing algorithm, called Vonode, has been developed specifically for analysis of such high-resolution tandem mass spectra. To fully exploit the high mass accuracy of these spectra, a unique scoring system is proposed to evaluate sequence tags based primarily on mass accuracy information of fragment ions. Consensus sequence tags were inferred for 11,422 spectra with an average peptide length of 5.5 residues from a total of 40,297 input spectra acquired in a 24-hour proteomics measurement of Rhodopseudomonas palustris. The accuracy of inferred consensus sequence tags was 84%. According to our comparison, the performance of Vonode was shown to be superior to the PepNovo v2.0 algorithm, in terms of the number of de novo sequenced spectra and the sequencing accuracy.ConclusionsHere, we improved de novo sequencing performance by developing a new algorithm specifically for high-resolution tandem mass spectral data. The Vonode algorithm is freely available for download at http://compbio.ornl.gov/Vonode.

Cultivation and quantitative proteomic analyses of acidophilic microbial communities

Acid mine drainage (AMD), an extreme environment characterized by low pH and high metal concentrations, can support dense acidophilic microbial biofilm communities that rely on chemoautotrophic production based on iron oxidation. Field determined production rates indicate that, despite the extreme conditions, these communities are sufficiently well adapted to their habitats to achieve primary production rates comparable to those of microbial communities occurring in some non-extreme environments. To enable laboratory studies of growth, production and ecology of AMD microbial communities, a culturing system was designed to reproduce natural biofilms, including organisms recalcitrant to cultivation. A comprehensive metabolic labeling-based quantitative proteomic analysis was used to verify that natural and laboratory communities were comparable at the functional level. Results confirmed that the composition and core metabolic activities of laboratory-grown communities were similar to a natural community, including the presence of active, low abundance bacteria and archaea that have not yet been isolated. However, laboratory growth rates were slow compared with natural communities, and this correlated with increased abundance of stress response proteins for the dominant bacteria in laboratory communities. Modification of cultivation conditions reduced the abundance of stress response proteins and increased laboratory community growth rates. The research presented here represents the first description of the application of a metabolic labeling-based quantitative proteomic analysis at the community level and resulted in a model microbial community system ideal for testing physiological and ecological hypotheses.

Characterization of anaerobic catabolism of p-coumarate in rhodopseudomonas palustris by integrating transcriptomics and quantitative proteomics

In this study, the pathway for anaerobic catabolism of p-coumarate by a model bacterium, Rhodopseudomonas palustris, was characterized by comparing the gene expression profiles of cultures grown in the presence of p-coumarate, benzoate, or succinate as the sole carbon sources. Gene expression was quantified at the mRNA level with transcriptomics and at the protein level with quantitative proteomics using 15N metabolic labeling. Protein relative abundances, along with their confidence intervals for statistical significance evaluation, were estimated with the software ProRata. Both -omics measurements were used as the transcriptomics provided near-full genome coverage of gene expression profiles and the quantitative proteomics ascertained abundance changes of over 1600 proteins. The integrated gene expression data are consistent with the hypothesis that p-coumarate is converted to benzoyl-CoA, which is then degraded via a known aromatic ring reduction pathway. For the metabolism of p-coumarate to benzoyl-CoA, two alternative routes, a β-oxidation route and a non-β-oxidation route, are possible. The integrated gene expression data provided strong support for the non-β-oxidation route in R. palustris. A putative gene was proposed for every step in the non-β-oxidation route.