Top-Down Characterization of the Post-Translationally Modified Intact Periplasmic Proteome from the Bacterium Novosphingobium aromaticivorans

In vitro anti-tumor activities of a novel recombinant immunotoxin targeting differentially overexpressed Leucine-rich repeat-containing G-protein-coupled receptor 5 in cervical cancer

OBJECTIVE

The study aims to develop a novel recombinant anti-LGR5 immunotoxin candidate based on a truncated form of Pseudomonas exotoxin A (ETA).

METHODS

To develop this LGR5-specific recombinant immunotoxin, a corresponding single chain antibody fragment (αLGR5(scFv)) fused to ETA, was expressed under osmotic stress in the presence of compatible solutes in Escherichia coli BL21 DE3 cells. Expression was monitored by Western blot analysis facilitated by an N-terminal 10x-His tag. Purification was done using immobilized metal affinity chromatography (IMAC) and size exclusion chromatography (SEC). The recombinant immunotoxin (rIT) was assessed for cell surface binding on cervical cancer cell lines using confocal microscopy and flow cytometry. The rIT was then used in an XTT cell viability assay to assess targeted cell killing.

RESULTS AND DISCUSSION

Upon confirmation of full-length protein by Western blot, purified protein was used to confirm binding on LGR5-positive cervical cancer cell lines via confocal microscopy and flow cytometry using anti-His PE antibody as a secondary antibody. Selective cell-killing of this novel recombinant immunotoxin was illustrated by the dose-dependent reduction in cell viability at IC50 values in nanomolar concentrations on antigen-positive but not antigen-negative cell lines.

CONCLUSIONS

In conclusion, the rIT described is a promising candidate to treat cervical cancer, which however, would finally need to be confirmed by preclinical in vivo studies.

Quantitative Top-down Proteomics Revealed Kinase Inhibitor-Induced Proteoform-Level Changes in Cancer Cells

Quantitative analysis of proteins and their post-translational modifications (PTMs) in complex biological samples is critical to understanding cellular biology as well as disease detection and treatment. Top-down proteomics methods provide a "bird's eye" view of the proteome by directly detecting and quantifying intact proteoforms. Here, we developed a high-throughput quantitative top-down proteomics platform to probe intact proteoform and phosphoproteoform abundance changes in HeLa cells as a result of treatment with staurosporine (STS), a broad-spectrum kinase inhibitor. In total, we identified and quantified 1187 proteoforms from 215 proteoform families. Among them, 55 proteoforms from 37 proteoform families were significantly changed upon STS treatment. These proteoforms were primarily related to catabolic, metabolic, and apoptotic pathways that are expected to be impacted as a result of kinase inhibition. In addition, we manually evaluated 25 proteoform families that expressed one or more phosphorylated proteoforms. We observed that phosphorylated proteoforms in the same proteoform family, such as eukaryotic initiation factor 4E binding protein 1 (4EBP1), were differentially regulated relative to the unphosphorylated proteoforms. Combining relative profiling of proteoforms within these proteoform families with individual proteoform profiling results in a more comprehensive picture of STS treatment-induced proteoform abundance changes that cannot be achieved using bottom-up methods.

Biochemical interactions between the Atm1-like transporter from Novosphingobium aromaticivorans and heavy metals

Novosphingobium aromaticivorans has the ability to survive in harsh environments by virtue of its suite of iron-containing oxygenases that biodegrade an astonishing array of aromatic compounds. It is also resistant to heavy metals through Atm1, an ATP-binding cassette protein that mediates active efflux of heavy metals conjugated to glutathione. However, Atm1 orthologues in higher organisms have been implicated in the intracellular transport of organic iron complexes. Our hypothesis suggests that the ability of Atm1 to remove heavy metals is related to the need for regulated iron handling in N. aromaticivorans to support high oxygenase activity. Here we provide the first data demonstrating a direct interaction between an iron-porphyrin compound (hemin) and NaAtm1. Hemin displayed considerably higher binding affinity and lower EC₅₀ to stimulate ATP hydrolysis by Atm1 than Ag-GSH, GSSG or GSH, established substrates of the transporter. Co-incubation of NaAtm1, hemin with Ag-GSH in ATPase assays revealed a non-competitive interaction, indicating distinct binding sites on NaAtm1 and this property was reinforced using molecular docking analysis. Our data suggests that NaAtm1 has considerable versatility in transporting organic conjugates of metals and that this versatility enables it to play roles in detoxification processes for toxic metals and in homeostasis of iron. The ability to play these distinct roles is enabled by the plasticity of the substrate binding site within the central cavity of NaAtm1.

Optimization of Higher-Energy Collisional Dissociation Fragmentation Energy for Intact Protein-Level Tandem Mass Tag Labeling

Isobaric chemical tag labeling (e.g., TMT) is a commonly used approach in quantitative proteomics, and quantification is enabled through detection of low-mass reporter ions generated after MS2 fragmentation. Recently, we have introduced and optimized an intact protein-level TMT labeling platform that demonstrated >90% labeling efficiency in complex samples with top-down proteomics. Higher-energy collisional dissociation (HCD) is commonly utilized for isobaric tag-labeled peptide fragmentation because it produces accurate reporter ion intensities and avoids loss of low mass ions. HCD energies have been optimized for isobaric tag labeled-peptides but have not been systematically evaluated for isobaric tag-labeled intact proteins. In this study, we report a systematic evaluation of normalized HCD fragmentation energies (NCEs) on TMT-labeled HeLa cell lysate using top-down proteomics. Our results suggested that reporter ions often result in higher ion intensities at higher NCEs. Optimal fragmentation of intact proteins for identification, however, required relatively lower NCE. We further demonstrated that a stepped NCE scheme with energies from 30% to 50% resulted in optimal quantification and identification of TMT-labeled HeLa proteins. These parameters resulted in an average reporter ion intensity of ∼4E4 and average proteoform spectrum matches (PrSMs) of >1000 per RPLC-MS/MS run with a 1% false discovery rate (FDR) cutoff.

A benchmarking protocol for intact protein-level Tandem Mass Tag (TMT) labeling for quantitative top-down proteomics

Isobaric chemical tag labeling for quantification of intact proteins in complex samples is limited due to the tendency of intact proteins precipitate under labeling conditions and increased sample complexity as a result of side products (i.e., incomplete labeling or labeling of unintended residues). To reduce precipitation under labeling conditions, we developed a technique to remove large proteoforms that allowed for the labeling and characterization of small proteoforms (<35 kDa) using top-down proteomics. We also systematically optimized protein-level Tandem Mass Tag (TMT) labeling conditions to obtain optimal labeling parameters for complex samples. Here, we present a benchmarking protocol for protein-level TMT labeling for quantitative top-down proteomics, including complex intact protein sample preparation, protein-level TMT labeling, top-down LC/MS analysis, and TMT reporter ion quantification.•

An optimized protocol for protein-level TMT labeling in complex sample.

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Limits production of incorrectly labeled side products for minimization of spectral complexity.

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A guideline for isobaric chemical tag quantification in top-down proteomics.

Optimization of protein-level tandem mass tag (TMT) labeling conditions in complex samples with top-down proteomics

Isobaric chemical tag labels (e.g., iTRAQ and TMT) have been extensively utilized as a standard quantification approach in bottom-up proteomics, which provides high multiplexing capacity and enables MS2-level quantification while not complicating the MS1 scans. We recently demonstrated the feasibility of intact protein TMT labeling for the identification and quantification with top-down proteomics of smaller intact proteoforms (<35 kDa) in complex biological samples through the removal of large proteins prior to labeling. Still, the production of side products during TMT labeling (i.e., incomplete labeling or labeling of unintended residues) complicated the analysis of complex protein samples. In this study, we systematically evaluated the protein-level TMT labeling reaction parameters, including TMT-to-protein mass ratio, pH/concentration of quenching buffer, protein concentration, reaction time, and reaction buffer. Our results indicated that: (1) high TMT-to-protein mass ratio (e.g., 8:1, 4:1), (2) high pH/concentration of quenching buffer (pH > 9.1, final hydroxylamine concentration >0.3%), and (3) high protein concentration (e.g., > 1.0 μg/μL) resulted in optimal labeling efficiency and minimized production of over/underlabeled side products. >90% labeling efficiency was achieved for E. coli cell lysate after optimization of protein-level TMT labeling conditions. In addition, a double labeling approach was developed for efficiently labeling limited biological samples with low concentrations. This research provides practical guidance for efficient TMT labeling of complex intact protein samples, which can be readily adopted in the high-throughput quantification top-down proteomics.

Optimization of a Top-Down Proteomics Platform for Closely Related Pathogenic Bacterial Discrimination

The current technique used for microbial identification in hospitals is matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). However, it suffers from important limitations, in particular, for closely related species or when the database used for the identification lacks the appropriate reference. In this work, we set up a liquid chromatography (LC)-MS/MS top-down proteomics platform, which aims at discriminating closely related pathogenic bacteria through the identification of specific proteoforms. Using Escherichia coli as a model, all steps of the workflow were optimized: protein extraction, on-line LC separation, MS method, and data analysis. Using optimized parameters, about 220 proteins, corresponding to more than 500 proteoforms, could be identified in a single run. We then used this platform for the discrimination of enterobacterial pathogens undistinguishable by MALDI-TOF, although leading to very different clinical outcomes. For each pathogen, we identified specific proteoforms that could potentially be used as biomarkers. We also improved the characterization of poorly described bacterial strains. Our results highlight the advantage of addressing proteoforms rather than peptides for accurate bacterial characterization and qualify top-down proteomics as a promising tool in clinical microbiology. Data are available via ProteomeXchange with the identifier PXD019247.

Deep Intact Proteoform Characterization in Human Cell Lysate Using High-pH and Low-pH Reversed-Phase Liquid Chromatography

Post-translational modifications (PTMs) play critical roles in biological processes and have significant effects on the structures and dynamics of proteins. Top-down proteomics methods were developed for and applied to the study of intact proteins and their PTMs in human samples. However, the large dynamic range and complexity of human samples makes the study of human proteins challenging. To address these challenges, we developed a 2D pH RP/RPLC-MS/MS technique that fuses high-resolution separation and intact protein characterization to study the human proteins in HeLa cell lysate. Our results provide a deep coverage of soluble proteins in human cancer cells. Compared to 225 proteoforms from 124 proteins identified when 1D separation was used, 2778 proteoforms from 628 proteins were detected and characterized using our 2D separation method. Many proteoforms with critically functional PTMs including phosphorylation were characterized. Additionally, we present the first detection of intact human GcvH proteoforms with rare modifications such as octanoylation and lipoylation. Overall, the increase in the number of proteoforms identified using 2DLC separation is largely due to the reduction in sample complexity through improved separation resolution, which enables the detection of low-abundance PTM-modified proteoforms. We demonstrate here that 2D pH RP/RPLC is an effective technique to analyze complex protein samples using top-down proteomics.

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

Top-down Mass Spectrometry Analysis of Human Serum Autoantibody Antigen-Binding Fragments

Detecting autoimmune diseases at an early stage is crucial for effective treatment and disease management to slow disease progression and prevent irreversible organ damage. In many autoimmune diseases, disease-specific autoantibodies are produced by B cells in response to soluble autoantigens due to defects in B cell tolerance mechanisms. Autoantibodies accrue early in disease development, and several are so disease-specific they serve as classification criteria. In this study, we established a high-throughput, sensitive, intact serum autoantibody analysis platform based on the optimization of a one dimensional ultra-high-pressure liquid chromatography top-down mass spectrometry platform (1D UPLC-TDMS). This approach has been successfully applied to a 12 standard monoclonal antibody antigen-binding fragment (Fab) mixture, demonstrating the feasibility to separate and sequence intact antibodies with high sequence coverage and high sensitivity. We then applied the optimized platform to characterize total serum antibody Fabs in a systemic lupus erythematosus (SLE) patient sample and compared it to healthy control samples. From this analysis, we show that the SLE sample has many dominant antibody Fab-related mass features unlike the healthy controls. To our knowledge, this is the first top-down demonstration of serum autoantibody pool analysis. Our proposed approach holds great promise for discovering novel serum autoantibody biomarkers that are of interest for diagnosis, prognosis, and tolerance induction, as well as improving our understanding of pathogenic autoimmune processes.

Comparative proteomic analysis of Xanthomonas citri ssp. citri periplasmic proteins reveals changes in cellular envelope metabolism during in vitro pathogenicity induction

Citrus canker is a plant disease caused by Gram-negative bacteria from the genus Xanthomonas. The most virulent species is Xanthomonas citri ssp. citri (XAC), which attacks a wide range of citrus hosts. Differential proteomic analysis of the periplasm-enriched fraction was performed for XAC cells grown in pathogenicity-inducing (XAM-M) and pathogenicity-non-inducing (nutrient broth) media using two-dimensional electrophoresis combined with liquid chromatography-tandem mass spectrometry. Amongst the 40 proteins identified, transglycosylase was detected in a highly abundant spot in XAC cells grown under inducing condition. Additional up-regulated proteins related to cellular envelope metabolism included glucose-1-phosphate thymidylyltransferase, dTDP-4-dehydrorhamnose-3,5-epimerase and peptidyl-prolyl cis-trans-isomerase. Phosphoglucomutase and superoxide dismutase proteins, known to be involved in pathogenicity in other Xanthomonas species or organisms, were also detected. Western blot and quantitative real-time polymerase chain reaction analyses for transglycosylase and superoxide dismutase confirmed that these proteins were up-regulated under inducing condition, consistent with the proteomic results. Multiple spots for the 60-kDa chaperonin and glyceraldehyde-3-phosphate dehydrogenase were identified, suggesting the presence of post-translational modifications. We propose that substantial alterations in cellular envelope metabolism occur during the XAC infectious process, which are related to several aspects, from defence against reactive oxygen species to exopolysaccharide synthesis. Our results provide new candidates for virulence-related proteins, whose abundance correlates with the induction of pathogenicity and virulence genes, such as hrpD6, hrpG, hrpB7, hpa1 and hrpX. The results present new potential targets against XAC to be investigated in further functional studies.

Top-down characterization of endogenous protein complexes with native proteomics

Protein complexes exhibit great diversity in protein membership, post-translational modifications and noncovalent cofactors, enabling them to function as the actuators of many important biological processes. The exposition of these molecular features using current methods lacks either throughput or molecular specificity, ultimately limiting the use of protein complexes as direct analytical targets in a wide range of applications. Here, we apply native proteomics, enabled by a multistage tandem MS approach, to characterize 125 intact endogenous complexes and 217 distinct proteoforms derived from mouse heart and human cancer cell lines in discovery mode. The native conditions preserved soluble protein-protein interactions, high-stoichiometry noncovalent cofactors, covalent modifications to cysteines, and, remarkably, superoxide ligands bound to the metal cofactor of superoxide dismutase 2. These data enable precise compositional analysis of protein complexes as they exist in the cell and demonstrate a new approach that uses MS as a bridge to structural biology.

Qualitative and quantitative characterization of protein biotherapeutics with liquid chromatography mass spectrometry

In the last decade, the advancement of liquid chromatography mass spectrometry (LC/MS) techniques has enabled their broad application in protein characterization, both quantitatively and qualitatively. Owing to certain important merits of LC/MS techniques (e.g., high selectivity, flexibility, and rapid method development), LC/MS assays are often deemed as preferable alternatives to conventional methods (e.g., ligand-binding assays) for the analysis of protein biotherapeutics. At the discovery and development stages, LC/MS is generally employed for two purposes absolute quantification of protein biotherapeutics in biological samples and qualitative characterization of proteins. For absolute quantification of a target protein in bio-matrices, recent work has led to improvements in the efficiency of LC/MS method development, sample treatment, enrichment and digestion, and high-performance low-flow-LC separation. These advances have enhanced analytical sensitivity, specificity, and robustness. As to qualitative analysis, a range of techniques have been developed to characterize intramolecular disulfide bonds, glycosylation, charge variants, primary sequence heterogeneity, and the drug-to-antibody ratio of antibody drug conjugate (ADC), which has enabled a refined ability to assess product quality. In this review, we will focus on the discussion of technical challenges and strategies of LC/MS-based quantification and characterization of biotherapeutics, with the emphasis on the analysis of antibody-based biotherapeutics such as monoclonal antibodies (mAbs) and ADCs. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:734-754, 2017.

MASH Suite Pro: A Comprehensive Software Tool for Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics is arguably a disruptive technology for the comprehensive analysis of all proteoforms arising from genetic variation, alternative splicing, and posttranslational modifications (PTMs). However, the complexity of top-down high-resolution mass spectra presents a significant challenge for data analysis. In contrast to the well-developed software packages available for data analysis in bottom-up proteomics, the data analysis tools in top-down proteomics remain underdeveloped. Moreover, despite recent efforts to develop algorithms and tools for the deconvolution of top-down high-resolution mass spectra and the identification of proteins from complex mixtures, a multifunctional software platform, which allows for the identification, quantitation, and characterization of proteoforms with visual validation, is still lacking. Herein, we have developed MASH Suite Pro, a comprehensive software tool for top-down proteomics with multifaceted functionality. MASH Suite Pro is capable of processing high-resolution MS and tandem MS (MS/MS) data using two deconvolution algorithms to optimize protein identification results. In addition, MASH Suite Pro allows for the characterization of PTMs and sequence variations, as well as the relative quantitation of multiple proteoforms in different experimental conditions. The program also provides visualization components for validation and correction of the computational outputs. Furthermore, MASH Suite Pro facilitates data reporting and presentation via direct output of the graphics. Thus, MASH Suite Pro significantly simplifies and speeds up the interpretation of high-resolution top-down proteomics data by integrating tools for protein identification, quantitation, characterization, and visual validation into a customizable and user-friendly interface. We envision that MASH Suite Pro will play an integral role in advancing the burgeoning field of top-down proteomics.

Top Down Proteomics Reveals Mature Proteoforms Expressed in Subcellular Fractions of the Echinococcus granulosus Preadult Stage

Echinococcus granulosus is the causative agent of cystic hydatid disease, a neglected zoonosis responsible for high morbidity and mortality. Several molecular mechanisms underlying parasite biology remain poorly understood. Here, E. granulosus subcellular fractions were analyzed by top down and bottom up proteomics for protein identification and characterization of co-translational and post-translational modifications (CTMs and PTMs, respectively). Nuclear and cytosolic extracts of E. granulosus protoscoleces were fractionated by 10% GELFrEE and proteins under 30 kDa were analyzed by LC-MS/MS. By top down analysis, 186 proteins and 207 proteoforms were identified, of which 122 and 52 proteoforms were exclusively detected in nuclear and cytosolic fractions, respectively. CTMs were evident as 71% of the proteoforms had methionine excised and 47% were N-terminal acetylated. In addition, in silico internal acetylation prediction coupled with top down MS allowed the characterization of 9 proteins differentially acetylated, including histones. Bottom up analysis increased the overall number of identified proteins in nuclear and cytosolic fractions to 154 and 112, respectively. Overall, our results provided the first description of the low mass proteome of E. granulosus subcellular fractions and highlighted proteoforms with CTMs and PTMS whose characterization may lead to another level of understanding about molecular mechanisms controlling parasitic flatworm biology.

Non-proteolytic functions of microbial proteases increase pathological complexity

Proteases are enzymes that catalyse hydrolysis of peptide bonds thereby controlling the shape, size, function, composition, turnover and degradation of other proteins. In microbes, proteases are often identified as important virulence factors and as such have been targets for novel drug design. It is emerging that some proteases possess additional non‐proteolytic functions that play important roles in host epithelia adhesion, tissue invasion and in modulating immune responses. These additional “moonlighting” functions have the potential to obfuscate data interpretation and have implications for therapeutic design. Moonlighting enzymes comprise a subcategory of multifunctional proteins that possess at least two distinct biological functions on a single polypeptide chain. Presently, identifying moonlighting proteins relies heavily on serendipitous empirical data with clues arising from proteins lacking signal peptides that are localised to the cell surface. Here, we describe examples of microbial proteases with additional non‐proteolytic functions, including streptococcal pyrogenic exotoxin B, PepO and C5a peptidases, mycoplasmal aminopeptidases, mycobacterial chaperones and viral papain‐like proteases. We explore how these non‐proteolytic functions contribute to host cell adhesion, modulate the coagulation pathway, assist in non‐covalent folding of proteins, participate in cell signalling, and increase substrate repertoire. We conclude by describing how proteomics has aided in moonlighting protein discovery, focusing attention on potential moonlighters in microbial exoproteomes.

Proteogenomics in microbiology: taking the right turn at the junction of genomics and proteomics

High-accuracy and high-throughput proteomic methods have completely changed the way we can identify and characterize proteins. MS-based proteomics can now provide a unique supplement to genomic data and add a new level of information to the interpretation of genomic sequences. Proteomics-driven genome annotation has become especially relevant in microbiology where genomes are sequenced on a daily basis and limitations of an in silico driven annotation process are well recognized. In this review paper, we outline different strategies on how one can design a proteogenomic experiment, for example on genome-sequenced (synonymous proteogenomics) versus unsequenced organisms (ortho-proteogenomics) or with the aid of other "omic" data such as RNA-seq. We touch upon many challenges that are encountered during a typical proteogenomic study, mostly concerning bioinformatics methods and downstream data analysis, but also related to creation and use of sequence databases. A large list of proteogenomic case studies of different microorganisms is provided to illustrate the mapping of MS/MS-derived peptide spectra to genomic DNA sequences. These investigations have led to accurate determination of translational initiation sites, pointed out eventual read-throughs or programmed frameshifts, detected signal peptide processing or other protein maturation events, removed questionable annotation assignments, and provided evidence for predicted hypothetical proteins.

Top Down proteomics: facts and perspectives

The rise of the "Top Down" method in the field of mass spectrometry-based proteomics has ushered in a new age of promise and challenge for the characterization and identification of proteins. Injecting intact proteins into the mass spectrometer allows for better characterization of post-translational modifications and avoids several of the serious "inference" problems associated with peptide-based proteomics. However, successful implementation of a Top Down approach to endogenous or other biologically relevant samples often requires the use of one or more forms of separation prior to mass spectrometric analysis, which have only begun to mature for whole protein MS. Recent advances in instrumentation have been used in conjunction with new ion fragmentation using photons and electrons that allow for better (and often complete) protein characterization on cases simply not tractable even just a few years ago. Finally, the use of native electrospray mass spectrometry has shown great promise for the identification and characterization of whole protein complexes in the 100 kDa to 1 MDa regime, with prospects for complete compositional analysis for endogenous protein assemblies a viable goal over the coming few years.

Ultraviolet photodissociation for characterization of whole proteins on a chromatographic time scale

Intact protein characterization using mass spectrometry thus far has been achieved at the cost of throughput. Presented here is the application of 193 nm ultraviolet photodissociation (UVPD) for top down identification and characterization of proteins in complex mixtures in an online fashion. Liquid chromatographic separation at the intact protein level coupled with fast UVPD and high-resolution detection resulted in confident identification of 46 unique sequences compared to 44 using HCD from prepared Escherichia coli ribosomes. Importantly, nearly all proteins identified in both the UVPD and optimized HCD analyses demonstrated a substantial increase in confidence in identification (as defined by an average decrease in E value of ∼40 orders of magnitude) due to the higher number of matched fragment ions. Also shown is the potential for high-throughput characterization of intact proteins via liquid chromatography (LC)-UVPD-MS of molecular weight-based fractions of a Saccharomyces cerevisiae lysate. In total, protein products from 215 genes were identified and found in 292 distinct proteoforms, 168 of which contained some type of post-translational modification.

Proteome-wide identification of predominant subcellular protein localizations in a bacterial model organism

Proteomics data provide unique insights into biological systems, including the predominant subcellular localization (SCL) of proteins, which can reveal important clues about their functions. Here we analyzed data of a complete prokaryotic proteome expressed under two conditions mimicking interaction of the emerging pathogen Bartonella henselae with its mammalian host. Normalized spectral count data from cytoplasmic, total membrane, inner and outer membrane fractions allowed us to identify the predominant SCL for 82% of the identified proteins. The spectral count proportion of total membrane versus cytoplasmic fractions indicated the propensity of cytoplasmic proteins to co-fractionate with the inner membrane, and enabled us to distinguish cytoplasmic, peripheral inner membrane and bona fide inner membrane proteins. Principal component analysis and k-nearest neighbor classification training on selected marker proteins or predominantly localized proteins, allowed us to determine an extensive catalog of at least 74 expressed outer membrane proteins, and to extend the SCL assignment to 94% of the identified proteins, including 18% where in silico methods gave no prediction. Suitable experimental proteomics data combined with straightforward computational approaches can thus identify the predominant SCL on a proteome-wide scale. Finally, we present a conceptual approach to identify proteins potentially changing their SCL in a condition-dependent fashion.

BIOLOGICAL SIGNIFICANCE

The work presented here describes the first prokaryotic proteome-wide subcellular localization (SCL) dataset for the emerging pathogen B. henselae (Bhen). The study indicates that suitable subcellular fractionation experiments combined with straight-forward computational analysis approaches assessing the proportion of spectral counts observed in different subcellular fractions are powerful for determining the predominant SCL of a large percentage of the experimentally observed proteins. This includes numerous cases where in silico prediction methods do not provide any prediction. Avoiding a treatment with harsh conditions, cytoplasmic proteins tend to co-fractionate with proteins of the inner membrane fraction, indicative of close functional interactions. The spectral count proportion (SCP) of total membrane versus cytoplasmic fractions allowed us to obtain a good indication about the relative proximity of individual protein complex members to the inner membrane. Using principal component analysis and k-nearest neighbor approaches, we were able to extend the percentage of proteins with a predominant experimental localization to over 90% of all expressed proteins and identified a set of at least 74 outer membrane (OM) proteins. In general, OM proteins represent a rich source of candidates for the development of urgently needed new therapeutics in combat of resurgence of infectious disease and multi-drug resistant bacteria. Finally, by comparing the data from two infection biology relevant conditions, we conceptually explore methods to identify and visualize potential candidates that may partially change their SCL in these different conditions. The data are made available to researchers as a SCL compendium for Bhen and as an assistance in further improving in silico SCL prediction algorithms.