Optimizing a Proteomics Platform for Urine Biomarker Discovery

Biomarker discovery approaches in urine have been hindered by concerns for reproducibility and inadequate standardization of proteomics protocols. In this study, we describe an optimized quantitative proteomics strategy for urine biomarker discovery, which is applicable to fresh or long frozen samples. We used urine from healthy controls to standardize iTRAQ (isobaric tags for relative and absolute quantitation) for variation induced by protease inhibitors, starting protein and iTRAQ label quantities, protein extraction methods, and depletion of albumin and immunoglobulin G (IgG). We observed the following: (a) Absence of protease inhibitors did not affect the number or identity of the high confidence proteins. (b) Use of less than 20 μg of protein per sample led to a significant drop in the number of identified proteins. (c) Use of as little as a quarter unit of an iTRAQ label did not affect the number or identity of the identified proteins. (d) Protein extraction by methanol precipitation led to the highest protein yields and the most reproducible spectra. (e) Depletion of albumin and IgG did not increase the number of identified proteins or deepen the proteome coverage. Applying this optimized protocol to four pairs of long frozen urine samples from diabetic Pima Indians with or without nephropathy, we observed patterns suggesting segregation of cases and controls by iTRAQ spectra. We also identified several previously reported candidate biomarkers that showed trends toward differential expression, albeit not reaching statistical significance in this small sample set.With ongoing advances in mass spectrometry (MS) and proteomics technology, proteomics analysis is progressively occupying a central position in biomarker discovery platforms. Biofluids such as urine and blood are the preferred media for proteomics analysis because of their ease of collection and extensive history of use in clinical laboratory practice. Urine, in particular, is an information-rich fluid that can be collected non-invasively and in large quantities. Many urine proteins are produced or shed in the kidney and urogenital tract (1), making urine a promising proximal source of biomarkers for diseases affecting these structures.However, proteomics-based biomarker discovery in urine faces multiple challenges. Urine proteomics is complicated by low urine protein concentration, variations in pH, and high concentrations of salts and urea or other urine components that interfere with sample processing. The urine proteome can also change with individual variables such as hydration, diurnal change, diet, and physical activity as well as variation in sample collection, processing, and storage. In addition, urine proteomics shares the usual challenges of biomarker discovery in other biofluids such as throughput, cost, and the need for a reproducible and quantitative work flow.Isotopic or isobaric labeling methods to reduce variation, increase throughput, and enable quantitative analysis have been developed to address some of these challenges. One such method, isobaric tags for relative and absolute quantitation (iTRAQ)¹ (2), combines relative and absolute peptide quantification with multiplexing ability to enable an increased throughput as well as simultaneous comparison of up to eight samples within one experimental run. Variations induced by urine sample processing have been systematically evaluated for proteomics analyses using two-dimensional gel electrophoresis (3–6), differential gel electrophoresis (7), and liquid chromatography-coupled mass spectrometry (LC-MS) (5, 8, 9). However, no systematic analyses of urine sample collection and processing have been reported for iTRAQ.Before utilizing iTRAQ-based quantitative proteomics for urine biomarker discovery, we evaluated the impact of variation in several processing steps (addition of protease inhibitors, the starting protein quantities, quantity of the iTRAQ label, protein extraction methods, and depletion of abundant proteins) on iTRAQ protein identification and quantitation. Applying this optimized biomarker discovery protocol to small quantities of long frozen urine samples from the Pima longitudinal study of diabetic nephropathy, we observed patterns suggestive of segregation of cases and controls by iTRAQ spectra. We also observed trends toward differential expression in several proteins that had been identified as putative biomarkers in previous studies. However, given the small sample size, none of these proteins retained statistical significance after multiple testing correction.     

From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions

Accurate measurements of cellular protein concentrations are invaluable to quantitative studies of gene expression and physiology in living cells. Here, we developed a versatile mass spectrometric workflow based on data‐independent acquisition proteomics (DIA/SWATH) together with a novel protein inference algorithm (xTop). We used this workflow to accurately quantify absolute protein abundances in Escherichia coli for > 2,000 proteins over > 60 growth conditions, including nutrient limitations, non‐metabolic stresses, and non‐planktonic states. The resulting high‐quality dataset of protein mass fractions allowed us to characterize proteome responses from a coarse (groups of related proteins) to a fine (individual) protein level. Hereby, a plethora of novel biological findings could be elucidated, including the generic upregulation of low‐abundant proteins under various metabolic limitations, the non‐specificity of catabolic enzymes upregulated under carbon limitation, the lack of large‐scale proteome reallocation under stress compared to nutrient limitations, as well as surprising strain‐dependent effects important for biofilm formation. These results present valuable resources for the systems biology community and can be used for future multi‐omics studies of gene regulation and metabolic control in E. coli.     

Mass Spectrometry-Based Approaches Toward Absolute Quantitative Proteomics

Mass spectrometry has served as a major tool for the discipline of proteomics to catalogue proteins in an unprecedented scale. With chemical and metabolic techniques for stable isotope labeling developed over the past decade, it is now routinely used as a method for relative quantification to provide valuable information on alteration of protein abundance in a proteome-wide scale. More recently, absolute or stoichiometric quantification of proteome is becoming feasible, in particular, with the development of strategies with isotope-labeled standards composed of concatenated peptides. On the other hand, remarkable progress has been also made in label-free quantification methods based on the number of identified peptides. Here we review these mass spectrometry-based approaches for absolute quantification of proteome and discuss their implications.Key Words: Quantitative proteomics, mass spectrometry, absolute quantification, stable isotope labeling, label-free.     

13C metabolic flux analysis of microbial and mammalian systems is enhanced with GC-MS measurements of glycogen and RNA labeling

¹³C metabolic flux analysis (¹³C-MFA) is a widely used tool for quantitative analysis of microbial and mammalian metabolism. Until now, ¹³C-MFA was based mainly on measurements of isotopic labeling of amino acids derived from hydrolyzed biomass proteins and isotopic labeling of extracted intracellular metabolites. Here, we demonstrate that isotopic labeling of glycogen and RNA, measured with gas chromatography-mass spectrometry (GC-MS), provides valuable additional information for ¹³C-MFA. Specifically, we demonstrate that isotopic labeling of glucose moiety of glycogen and ribose moiety of RNA greatly enhances resolution of metabolic fluxes in the upper part of metabolism; importantly, these measurements allow precise quantification of net and exchange fluxes in the pentose phosphate pathway. To demonstrate the practical importance of these measurements for ¹³C-MFA, we have used Escherichia coli as a model microbial system and CHO cells as a model mammalian system. Additionally, we have applied this approach to determine metabolic fluxes of glucose and xylose co-utilization in the E. coli ΔptsG mutant. The convenience of measuring glycogen and RNA, which are stable and abundant in microbial and mammalian cells, offers the following key advantages: reduced sample size, no quenching required, no extractions required, and GC-MS can be used instead of more costly LC-MS/MS techniques. Overall, the presented approach for ¹³C-MFA will have widespread applicability in metabolic engineering and biomedical research.     

In silico Proteome-wide Amino aCid and Elemental Composition (PACE) Analysis of Expression Proteomics Data Provides A Fingerprint of Dominant Metabolic Processes

Proteome-wide Amino aCid and Elemental composition (PACE) analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decomposition of proteins detected and quantified in a proteomics experiment into 20 amino acids and five elements (C, H, N, O and S), with protein abundances converted to relative abundances of amino acids and elements. The method is robust and very sensitive; it provides statistically reliable differentiation between very similar proteomes. In addition, PACE provides novel insights into proteome-wide metabolic processes, occurring, e.g., during cell starvation. For instance, both Escherichia coli and Synechocystis down-regulate sulfur-rich proteins upon sulfur deprivation, but E. coli preferentially down-regulates cysteine-rich proteins while Synechocystis mainly down-regulates methionine-rich proteins. Due to its relative simplicity, flexibility, generality and wide applicability, PACE analysis has the potential of becoming a standard analytical tool in proteomics.     

An open source cryostage and software analysis method for detection of antifreeze activity

The aim of this study is to provide the reader with a simple setup that can detect antifreeze proteins (AFP) by inhibition of ice recrystallisation in very small sample sizes. This includes an open source cryostage, a method for preparing and loading samples as well as a software analysis method. The entire setup was tested using hyperactive AFP from the cerambycid beetle, Rhagium mordax. Samples containing AFP were compared to buffer samples, and the results are visualised as crystal radius evolution over time and in absolute change over 30 min. Statistical analysis showed that samples containing AFP could reliably be told apart from controls after only two minutes of recrystallisation. The goal of providing a fast, cheap and easy method for detecting antifreeze proteins in solution was met, and further development of the system can be followed at https://github.com/pechano/cryostage.     

An engineered non-oxidative glycolysis pathway for acetone production in <Emphasis Type="Italic">Escherichia coli</Emphasis>

Objectives

To find new metabolic engineering strategies to improve the yield of acetone in Escherichia coli.

Results

Results of flux balance analysis from a modified Escherichia coli genome-scale metabolic network suggested that the introduction of a non-oxidative glycolysis (NOG) pathway would improve the theoretical acetone yield from 1 to 1.5 mol acetone/mol glucose. By inserting the fxpk gene encoding phosphoketolase from Bifidobacterium adolescentis into the genome, we constructed a NOG pathway in E.coli. The resulting strain produced 47 mM acetone from glucose under aerobic conditions in shake-flasks. The yield of acetone was improved from 0.38 to 0.47 mol acetone/mol glucose which is a significant over the parent strain.

Conclusions

Guided by computational analysis of metabolic networks, we introduced a NOG pathway into E. coli and increased the yield of acetone, which demonstrates the importance of modeling analysis for the novel metabolic engineering strategies.

     

Synthesis,antimicrobial, cytotoxic and E. coli DNA gyrase inhibitory activities of coumarinyl amino alcohols

Here we report the in vitro antimicrobial activity (minimum inhibitory concentration) of fourteen coumarinyl amino alcohols 2–16 against eight bacterial strains and two fungi. Among these compounds 4, 8, 12, 15 and 16 showed moderate to good microbial inhibition with MIC values varied from 6.25 to 25 μg/mL. The most promising compounds were also evaluated for their in vitro cytotoxic and E. coli DNA gyrase inhibitory activities along with the two 7-oxy-4-methyl coumarinyl amino alcohol derivatives 17 and 18, which were found to be the most potent in in vitro antimicrobial screening in our previous study. All the active compounds, including 17 and 18, were also docked into the E. coli DNA gyrase ATP binding site (PDB ID: 1KZN) to investigate their binding interactions. Of these compound 17 has shown maximum binding energy value of −6.13 kcal/mol.     

Regulation of Arabinose and Xylose Metabolism in Escherichia coli

Bacteria such as Escherichia coli will often consume one sugar at a time when fed multiple sugars, in a process known as carbon catabolite repression. The classic example involves glucose and lactose, where E. coli will first consume glucose, and only when it has consumed all of the glucose will it begin to consume lactose. In addition to that of lactose, glucose also represses the consumption of many other sugars, including arabinose and xylose. In this work, we characterized a second hierarchy in E. coli, that between arabinose and xylose. We show that, when grown in a mixture of the two pentoses, E. coli will consume arabinose before it consumes xylose. Consistent with a mechanism involving catabolite repression, the expression of the xylose metabolic genes is repressed in the presence of arabinose. We found that this repression is AraC dependent and involves a mechanism where arabinose-bound AraC binds to the xylose promoters and represses gene expression. Collectively, these results demonstrate that sugar utilization in E. coli involves multiple layers of regulation, where cells will consume first glucose, then arabinose, and finally xylose. These results may be pertinent in the metabolic engineering of E. coli strains capable of producing chemical and biofuels from mixtures of hexose and pentose sugars derived from plant biomass.The transporters and enzymes in many sugar metabolic pathways are conditionally expressed in response to their cognate sugar or a downstream pathway intermediate. While the induction of these pathways in response to a single sugar has been studied extensively (28), far less is known about how these pathways are induced in response to multiple sugars. One notable exception is the phenomenon observed when bacteria are grown in the presence of glucose and another sugar (10, 15). In such mixtures, the bacteria will often consume glucose first before consuming the other sugar, a process known as carbon catabolite repression (27). The classic example of carbon catabolite repression is the diauxic shift seen in the growth of Escherichia coli on mixtures of glucose and lactose, where the cells first consume glucose before consuming lactose. When the cells are consuming glucose, the genes in the lactose metabolic pathway are not induced, thus preventing the sugar from being consumed. A number of molecules participate in this regulation, including the cyclic AMP receptor protein (CRP), adenylate cyclase, cyclic AMP (cAMP), and EIIA from the phosphoenolpyruvate:glucose phosphotransferase system (PTS) (33). In addition to lactose, the metabolic genes for many other sugars are subject to catabolite repression by glucose in E. coli (27). While the preferential utilization of glucose is well known, it is an open question whether additional hierarchies exist among other sugars.Recently, substantial effort has been directed toward developing microorganisms capable of producing chemicals and biofuels from plant biomass (1, 34, 42). After glucose, l-arabinose and d-xylose are the next most abundant sugars found in plant biomass. Therefore, a key step in producing various chemicals and fuels from plant biomass will be the engineering of strains capable of efficiently fermenting these three sugars. However, one challenge concerns catabolite repression, which prevents microorganisms from fermenting these three sugars simultaneously and, as a consequence, may decrease the efficiency of the fermentation process. E. coli cells will first consume glucose before consuming either arabinose or xylose. As in the case of lactose, the genes in the arabinose and xylose metabolic pathways are not expressed when glucose is being consumed. In addition to glucose catabolite repression, a second hierarchy, between arabinose and xylose, appears to exist. Kang and coworkers have observed that the genes in the xylose metabolic pathway were repressed when cells were grown in a mixture of arabinose and xylose (21). Hernandez-Montalvo and coworkers also observed that E. coli utilizes arabinose before xylose (19). While a number of strategies exist for breaking the glucose-mediated repression of arabinose and xylose metabolism (8, 16, 19, 31), none exist for breaking the arabinose-mediated repression of xylose metabolism. Moreover, little is known about this repression beyond the observations made by these researchers.In this work, we investigate how the arabinose and xylose metabolic pathways are jointly regulated. We demonstrate that E. coli will consume arabinose before consuming xylose when it is grown in a mixture of the two sugars. Consistent with a mechanism involving catabolite repression, the genes in the xylose metabolic pathway are repressed in the presence of arabinose. We found that this repression is AraC dependent and is most likely due to binding by arabinose-bound AraC to the xylose promoters, with consequent inhibition of gene expression.     

Consequences of the overexpression of a eukaryotic membrane protein, the human KDEL receptor, in Escherichia coli

Escherichia coli is the most widely used host for producing membrane proteins. Thus far, to study the consequences of membrane protein overexpression in E. coli, we have focussed on prokaryotic membrane proteins as overexpression targets. Their overexpression results in the saturation of the Sec translocon, which is a protein-conducting channel in the cytoplasmic membrane that mediates both protein translocation and insertion. Saturation of the Sec translocon leads to (i) protein misfolding/aggregation in the cytoplasm, (ii) impaired respiration, and (iii) activation of the Arc response, which leads to inefficient ATP production and the formation of acetate. The overexpression yields of eukaryotic membrane proteins in E. coli are usually much lower than those of prokaryotic ones. This may be due to differences between the consequences of the overexpression of prokaryotic and eukaryotic membrane proteins in E. coli. Therefore, we have now also studied in detail how the overexpression of a eukaryotic membrane protein, the human KDEL receptor, affects E. coli. Surprisingly, the consequences of the overexpression of a prokaryotic and a eukaryotic membrane protein are very similar. Strain engineering and likely also protein engineering can be used to remedy the saturation of the Sec translocon upon overexpression of both prokaryotic and eukaryotic membrane proteins in E. coli.     

Morphology and SSU rDNA-based phylogeny of two Euplotes species from China: E. wuhanensis sp. n. and E. muscicola Kahl, 1932 (Ciliophora,Euplotida)

The living morphology, infraciliature and silverline system of two small Euplotes species, E. wuhanensis sp. n. and E. muscicola Kahl, 1932, isolated from Wuhan, central China, were investigated. Euplotes wuhanensis sp. n. is characterized by a combination of features including small size (40–50 × 25–30 μm), two conspicuously small and eight normal-sized frontoventral cirri, five transverse cirri in two groups, two marginal and two caudal cirri, seven dorsal kineties with about 12 dikinetids in the mid-dorsal row and a double-eurystomus type of dorsal silverline pattern. The Wuhan population of E. muscicola closely resembles previously described populations. The establishments of three subspecies of E. muscicola are not supported. The small subunit ribosomal RNA gene sequences were determined for both species. We propose that the two sequences under the name of E. muscicola (No. AJ305254, DQ917684 deposited in GenBank) are very likely from misidentified material. Phylogenetic analyses based on these data support the validity of both E. muscicola and E. wuhanensis as distinct species.     

Metabolic flux balance analysis and the in silicoanalysis of Escherichia coliK-12 gene deletions

Background

Genome sequencing and bioinformatics are producing detailed lists of the molecular components contained in many prokaryotic organisms. From this 'parts catalogue' of a microbial cell, in silicorepresentations of integrated metabolic functions can be constructed and analyzed using flux balance analysis (FBA). FBA is particularly well-suited to study metabolic networks based on genomic, biochemical, and strain specific information.

Results

Herein, we have utilized FBA to interpret and analyze the metabolic capabilities of Escherichia coli. We have computationally mapped the metabolic capabilities of E. coliusing FBA and examined the optimal utilization of the E. colimetabolic pathways as a function of environmental variables. We have used an in silicoanalysis to identify seven gene products of central metabolism (glycolysis, pentose phosphate pathway, TCA cycle, electron transport system) essential for aerobic growth of E. colion glucose minimal media, and 15 gene products essential for anaerobic growth on glucose minimal media. The in silico tpi ^-, zwf, and pta ^-mutant strains were examined in more detail by mapping the capabilities of these in silicoisogenic strains.

Conclusions

We found that computational models of E. colimetabolism based on physicochemical constraints can be used to interpret mutant behavior. These in silicaresults lead to a further understanding of the complex genotype-phenotype relation. Supplementary information: 10.1186/1471-2105-1-1     

Characterization of low-molecular-weight-glutenin subunit genes from the D-genome of Triticum aestivum,Aegilops crassa,Ae. cylindrica and Ae. tauschii

Twenty low-molecular-weight-glutenin subunit (LMW-GS) gene sequences from the D-genome from Aegilops crassa (2n = 4x = 28), Ae. cylindrica (2n = 4x = 28), Ae. tauschii (2n = 2x = 14) and Triticum aestivum (2n = 6x = 42) were obtained using five sets of specific allele primer pairs. Only the sequences of the first primer pair were complete coding sequences (cds) of LMW-GS, and had 305, 304, 306 and 305 LMW-m amino acid residues in Ae. crassa, Ae. cylindrica, Ae. tauschii and T. aestivum, respectively. The repetitive domain and repeat motif numbers of all LMW glutenin subunits showed eight conserved cysteine residues that lead to the same functional activity in different genome. Based on DNA and predicted protein sequences, phylogenetic trees for all sets of sequences were drawn. At the DNA level, the species closest to T. aestivum for the second, third, fourth and fifth set of sequences were Ae. cylindrica, Ae. tauschii and Ae. crassa, respectively. At the protein level, the species closest to T. aestivum based on the first, second and fifth set of sequences were Ae. cylindrica, Ae. crassa and Ae. crassa, respectively. For other sets of sequences, bread wheat proved to be a distinct species. The LMW-GS gene sequences have been recorded in the GenBank with accession numbers JQ726549–JQ726568JQ726549JQ726550JQ726551JQ726552JQ726553JQ726554JQ726555JQ726556JQ726557JQ726558JQ726559JQ726560JQ726561JQ726562JQ726563JQ726564JQ726565JQ726566JQ726567JQ726568.     

Pyruvate decarboxylase and alcohol dehydrogenase overexpression in Escherichia coli resulted in high ethanol production and rewired metabolic enzyme networks

Pyruvate decarboxylase and alcohol dehydrogenase are efficient enzymes for ethanol production in Zymomonas mobilis. These two enzymes were over-expressed in Escherichia coli, a promising candidate for industrial ethanol production, resulting in high ethanol production in the engineered E. coli. To investigate the intracellular changes to the enzyme overexpression for homoethanol production, 2-DE and LC–MS/MS were performed. More than 1,000 protein spots were reproducibly detected in the gel by image analysis. Compared to the wild-type, 99 protein spots showed significant changes in abundance in the recombinant E. coli, in which 46 were down-regulated and 53 were up-regulated. Most proteins related to tricarboxylic acid cycle, glycerol metabolism and other energy metabolism were up-regulated, whereas proteins involved in glycolysis and glyoxylate pathway were down-regulated, indicating the rewired metabolism in the engineered E. coli. As glycolysis is the main pathway for ethanol production, and it was inhibited significantly in engineered E. coli, further efforts should be directed at minimizing the repression of glycolysis to optimize metabolism network for higher yields of ethanol production.     

Complete Genome Sequence and Comparative Metabolic Profiling of the Prototypical Enteroaggregative Escherichia coli Strain 042

Background

Escherichia coli can experience a multifaceted life, in some cases acting as a commensal while in other cases causing intestinal and/or extraintestinal disease. Several studies suggest enteroaggregative E. coli are the predominant cause of E. coli-mediated diarrhea in the developed world and are second only to Campylobacter sp. as a cause of bacterial-mediated diarrhea. Furthermore, enteroaggregative E. coli are a predominant cause of persistent diarrhea in the developing world where infection has been associated with malnourishment and growth retardation.

Methods

In this study we determined the complete genomic sequence of E. coli 042, the prototypical member of the enteroaggregative E. coli, which has been shown to cause disease in volunteer studies. We performed genomic and phylogenetic comparisons with other E. coli strains revealing previously uncharacterised virulence factors including a variety of secreted proteins and a capsular polysaccharide biosynthetic locus. In addition, by using Biolog™ Phenotype Microarrays we have provided a full metabolic profiling of E. coli 042 and the non-pathogenic lab strain E. coli K-12. We have highlighted the genetic basis for many of the metabolic differences between E. coli 042 and E. coli K-12.

Conclusion

This study provides a genetic context for the vast amount of experimental and epidemiological data published thus far and provides a template for future diagnostic and intervention strategies.