Data extraction from proteomics raw data: an evaluation of nine tandem MS tools using a large Orbitrap data set

Gene expression regulation by modulating Hfq expression in coordination with tailor-made sRNA-based knockdown in Escherichia coli

The tailor-made synthetic sRNA-based gene expression knockdown system has demonstrated its efficacy in achieving pathway balancing in microbes, facilitating precise target gene repression and fine-tuned control of gene expression. This system operates under a competitive mode of gene regulation, wherein the tailor-made synthetic sRNA shares the intrinsic intracellular Hfq protein with other RNAs. The limited intracellular Hfq amount has the potential to become a constraining factor in the post-transcription regulation of sRNAs. To enhance the efficiency of the tailor-made sRNA gene expression regulation platform, we introduced an Hfq expression level modulation-coordinated sRNA-based gene knockdown system. This system comprises tailor-made sRNA expression cassettes that produce varying Hfq expression levels using different strength promoters. Modulating the expression levels of Hfq significantly improved the repressing capacity of sRNA, as evidenced by evaluations with four fluorescence proteins. In order to validate the practical application of this system, we applied the Hfq-modulated sRNA-based gene knockdown cassette to Escherichia coli strains producing 5-aminolevulinic acid and L-tyrosine. Diversifying the expression levels of metabolic enzymes through this cassette resulted in substantial increases of 74.6% in 5-aminolevulinic acid and 144% in L-tyrosine production. Tailor-made synthetic sRNA-based gene expression knockdown system, coupled with Hfq copy modulation, exhibits potential for optimizing metabolic fluxes through biosynthetic pathways, thereby enhancing the production yields of bioproducts.

Synthetic fused sRNA for the simultaneous repression of multiple genes

Efficient control over multiple gene expression still presents a major challenge. Synthetic sRNA enables targeted gene expression control in trans without directly modifying the chromosome, but its use to simultaneously target multiple genes can often cause cell growth defects because of the need for additional energy for transcription and lowering of their repression efficiency by limiting the amount of Hfq protein. To address these limitations, we present fusion sRNA (fsRNA) that simultaneously regulates the translation of multiple genes efficiently. It is constructed by linking the mRNA-binding modules for multiple targeted genes in one sRNA scaffold via one-pot generation using overlap extension PCR. The repression capacity of fsRNA was demonstrated by the construction of sRNAs to target four endogenous genes: caiF, hybG, ytfR and minD in Escherichia coli. Their cross-reactivity and the effect on cell growth were also investigated. As practical applications, we applied fsRNA to violacein- and protocatechuic acid-producing strains, resulting in increases of 13% violacein and 81% protocatechuic acid, respectively. The developed fsRNA-mediated multiple gene expression regulation system thus enables rapid and efficient development of optimised cell factories for valuable chemicals without cell growth defects and limiting cellular resources.Key points• Synthetic fusion sRNA (fsRNA)-based system was constructed for the repression of multiple target genes.• fsRNA repressed multiple genes by only expressing a single sRNA while minimising the cellular burden.• The application of fsRNA showed the increased production titers of violacein (13%) and protocatechuic acid (81%).

Stochastic Analysis Demonstrates the Dual Role of Hfq in Chaperoning E. coli Sugar Shock Response

Small RNAs (sRNAs) play a crucial role in the regulation of bacterial gene expression by silencing the translation of target mRNAs. SgrS is an sRNA that relieves glucose-phosphate stress, or "sugar shock" in E. coli. The power of single cell measurements is their ability to obtain population level statistics that illustrate cell-to-cell variation. Here, we utilize single molecule super-resolution microscopy in single E. coli cells coupled with stochastic modeling to analyze glucose-phosphate stress regulation by SgrS. We present a kinetic model that captures the combined effects of transcriptional regulation, gene replication and chaperone mediated RNA silencing in the SgrS regulatory network. This more complete kinetic description, simulated stochastically, recapitulates experimentally observed cellular heterogeneity and characterizes the binding of SgrS to the chaperone protein Hfq as a slow process that not only stabilizes SgrS but also may be critical in restructuring the sRNA to facilitate association with its target ptsG mRNA.

Comparative proteome analysis of propionate degradation by Syntrophobacter fumaroxidans in pure culture and in coculture with methanogens

Syntrophobacter fumaroxidans is a sulfate-reducing bacterium able to grow on propionate axenically or in syntrophic interaction with methanogens or other sulfate-reducing bacteria. We performed a proteome analysis of S. fumaroxidans growing with propionate axenically with sulfate or fumarate, and in syntrophy with Methanospirillum hungatei, Methanobacterium formicicum or Desulfovibrio desulfuricans. Special attention was put on the role of hydrogen and formate in interspecies electron transfer (IET) and energy conservation. Formate dehydrogenase Fdh1 and hydrogenase Hox were the main confurcating enzymes used for energy conservation. In the periplasm, Fdh2 and hydrogenase Hyn play an important role in reverse electron transport associated with succinate oxidation. Periplasmic Fdh3 and Fdh5 were involved in IET. The sulfate reduction pathway was poorly regulated and many enzymes associated with sulfate reduction (Sat, HppA, AprAB, DsrAB and DsrC) were abundant even at conditions where sulfate was not present. Proteins similar to heterodisulfide reductases (Hdr) were abundant. Hdr/Flox was detected in all conditions while HdrABC/HdrL was exclusively detected when sulfate was available; these complexes most likely confurcate electrons. Our results suggest that S. fumaroxidans mainly used formate for electron release and that different confurcating mechanisms were used in its sulfidogenic metabolism.

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.

Database Search Engines: Paradigms, Challenges and Solutions

The first step in identifying proteins from mass spectrometry based shotgun proteomics data is to infer peptides from tandem mass spectra, a task generally achieved using database search engines. In this chapter, the basic principles of database search engines are introduced with a focus on open source software, and the use of database search engines is demonstrated using the freely available SearchGUI interface. This chapter also discusses how to tackle general issues related to sequence database searching and shows how to minimize their impact.

Determination of key enzymes for threonine synthesis through in vitro metabolic pathway analysis

Background

The overexpression of key enzymes in a metabolic pathway is a frequently used genetic engineering strategy for strain improvement. Metabolic control analysis has been proposed to quantitatively determine key enzymes. However, the lack of quality data often makes it difficult to correctly identify key enzymes through control analysis. Here, we proposed a method combining in vitro metabolic pathway analysis and proteomics measurement to find the key enzymes in threonine synthesis pathway.

Results

All enzymes in the threonine synthesis pathway were purified for the reconstruction and perturbation of the in vitro pathway. Label-free proteomics technology combined with APEX (absolute protein expression measurements) data analysis method were employed to determine the absolute enzyme concentrations in the crude enzyme extract obtained from a threonine production strain during the fastest threonine production period. The flux control coefficient of each enzyme in the pathway was then calculated by measuring the flux changes after titration of the corresponding enzyme. The isoenzyme LysC catalyzing the first step in the pathway has the largest flux control coefficient, and thus its concentration change has the biggest impact on pathway flux. To verify that the key enzyme identified through in vitro pathway analysis is also the key enzyme in vivo, we overexpressed LysC in the original threonine production strain. Fermentation results showed that the threonine concentration was increased 30% and the yield was increased 20%.

Conclusions

In vitro metabolic pathways simulating in vivo cells can be built based on precise measurement of enzyme concentrations through proteomics technology and used for the determination of key enzymes through metabolic control analysis. This provides a new way to find gene overexpression targets for industrial strain improvement.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-015-0275-8) contains supplementary material, which is available to authorized users.

Proteomic analysis reveals metabolic and regulatory systems involved in the syntrophic and axenic lifestyle of Syntrophomonas wolfei

Microbial syntrophy is a vital metabolic interaction necessary for the complete oxidation of organic biomass to methane in all-anaerobic ecosystems. However, this process is thermodynamically constrained and represents an ecosystem-level metabolic bottleneck. To gain insight into the physiology of this process, a shotgun proteomics approach was used to quantify the protein landscape of the model syntrophic metabolizer, Syntrophomonas wolfei, grown axenically and syntrophically with Methanospirillum hungatei. Remarkably, the abundance of most proteins as represented by normalized spectral abundance factor (NSAF) value changed very little between the pure and coculture growth conditions. Among the most abundant proteins detected were GroEL and GroES chaperonins, a small heat shock protein, and proteins involved in electron transfer, beta-oxidation, and ATP synthesis. Several putative energy conservation enzyme systems that utilize NADH and ferredoxin were present. The abundance of an EtfAB2 and the membrane-bound iron-sulfur oxidoreductase (Swol_0698 gene product) delineated a potential conduit for electron transfer between acyl-CoA dehydrogenases and membrane redox carriers. Proteins detected only when S. wolfei was grown with M. hungatei included a zinc-dependent dehydrogenase with a GroES domain, whose gene is present in genomes in many organisms capable of syntrophy, and transcriptional regulators responsive to environmental stimuli or the physiological status of the cell. The proteomic analysis revealed an emphasis on macromolecular stability and energy metabolism by S. wolfei and presence of regulatory mechanisms responsive to external stimuli and cellular physiological status.

LC-MS spectra processing

Peak extraction from raw data is the first step in LC-MS data analysis. The quality of this procedure is important since it affects the quality and accuracy of all subsequent analysis such as database searches and peak quantitation. The most important and most accurately measured physical entity provided by mass spectrometers is m/z values which need to be extracted by state of art algorithms and scrutinized thoroughly. The aim of this chapter is to provide a discussion of peak processing methods and furthermore discuss some of the yet unresolved or neglected issues. A few novel concepts are also proposed for analysis and visualization. The final section of this chapter provides a note on possible software for spectra processing.

Algorithms for database-dependent search of MS/MS data

The frequent used bottom-up strategy for identification of proteins and their associated modifications generate nowadays typically thousands of MS/MS spectra that normally are matched automatically against a protein sequence database. Search engines that take as input MS/MS spectra and a protein sequence database are referred as database-dependent search engines. Many programs both commercial and freely available exist for database-dependent search of MS/MS spectra and most of the programs have excellent user documentation. The aim here is therefore to outline the algorithm strategy behind different search engines rather than providing software user manuals. The process of database-dependent search can be divided into search strategy, peptide scoring, protein scoring, and finally protein inference. Most efforts in the literature have been put in to comparing results from different software rather than discussing the underlining algorithms. Such practical comparisons can be cluttered by suboptimal implementation and the observed differences are frequently caused by software parameters settings which have not been set proper to allow even comparison. In other words an algorithmic idea can still be worth considering even if the software implementation has been demonstrated to be suboptimal. The aim in this chapter is therefore to split the algorithms for database-dependent searching of MS/MS data into the above steps so that the different algorithmic ideas become more transparent and comparable. Most search engines provide good implementations of the first three data analysis steps mentioned above, whereas the final step of protein inference are much less developed for most search engines and is in many cases performed by an external software. The final part of this chapter illustrates how protein inference is built into the VEMS search engine and discusses a stand-alone program SIR for protein inference that can import a Mascot search result.

ComplexQuant: high-throughput computational pipeline for the global quantitative analysis of endogenous soluble protein complexes using high resolution protein HPLC and precision label-free LC/MS/MS

The experimental isolation and characterization of stable multi-protein complexes are essential to understanding the molecular systems biology of a cell. To this end, we have developed a high-throughput proteomic platform for the systematic identification of native protein complexes based on extensive fractionation of soluble protein extracts by multi-bed ion exchange high performance liquid chromatography (IEX-HPLC) combined with exhaustive label-free LC/MS/MS shotgun profiling. To support these studies, we have built a companion data analysis software pipeline, termed ComplexQuant. Proteins present in the hundreds of fractions typically collected per experiment are first identified by exhaustively interrogating MS/MS spectra using multiple database search engines within an integrative probabilistic framework, while accounting for possible post-translation modifications. Protein abundance is then measured across the fractions based on normalized total spectral counts and precursor ion intensities using a dedicated tool, PepQuant. This analysis allows co-complex membership to be inferred based on the similarity of extracted protein co-elution profiles. Each computational step has been optimized for processing large-scale biochemical fractionation datasets, and the reliability of the integrated pipeline has been benchmarked extensively. This article is part of a Special Issue entitled: From protein structures to clinical applications.