Proteomic Profiling Experiments Research Articles

Plasma and Kidney Proteome Profiling Combined with Laser Capture Microdissection Reveal Large Increases in Immunoglobulins with Age.

One of the main hallmarks of aging is aging-associated inflammation, also known as inflammaging. In this study, by comparing plasma and kidney proteome profiling of young and old mice using LC-MS profiling, we discovered that immunoglobulins are the proteins that exhibit the highest increase with age. This observation seems to have been disregarded because conventional proteome profiling experiments typically overlook the expression of high-abundance proteins or employ depletion methods to remove them before LC-MS analysis. We show that proteome profiling of immunoglobulins will likely be a useful biomarker of aging. Spatial profiling using immunofluorescence staining of kidney sections indicates that the main increases in immunoglobulins with age are localized in the glomeruli of the kidney. Using laser capture microdissection coupled with LC-MS, we show an increase in multiple immune-related proteins in glomeruli from aged mice. Increased deposition of immunoglobulins, immune complexes, and complement proteins in the kidney glomeruli may be a factor leading to reduced filtering capacity of the kidney with age. Therapeutic strategies to reduce the deposition of immunoglobulins in the kidney may be an attractive strategy for healthy aging.

Label-Free Quantitative Thermal Proteome Profiling Reveals Target Transcription Factors with Activities Modulated by MC3R Signaling.

Thermal proteome profiling with label-free quantitation using ion-mobility-enhanced LC-MS offers versatile data sets, providing information on protein differential expression, thermal stability, and the activities of transcription factors. We developed a multidimensional data analysis workflow for label-free quantitative thermal proteome profiling (TPP) experiments that incorporates the aspects of gene set enrichment analysis, differential protein expression analysis, and inference of transcription factor activities from LC-MS data. We applied it to study the signaling processes downstream of melanocortin 3 receptor (MC3R) activation by endogenous agonists derived from the proopiomelanocortin prohormone: ACTH, α-MSH, and γ-MSH. The obtained information was used to map signaling pathways downstream of MC3R and to deduce transcription factors responsible for cellular response to ligand treatment. Using our workflow, we identified differentially expressed proteins and investigated their thermal stability. We found in total 298 proteins with altered thermal stability, resulting from MC3R activation. Out of these, several proteins were transcription factors, indicating them as being downstream target regulators that take part in the MC3R signaling cascade. We found transcription factors CCAR2, DDX21, HMGB2, SRSF7, and TET2 to have altered thermal stability. These apparent target transcription factors within the MC3R signaling cascade play important roles in immune responses. Additionally, we inferred the activities of the transcription factors identified in our data set. This was done with Bayesian statistics using the differential expression data we obtained with label-free quantitative LC-MS. The inferred transcription factor activities were validated in our bioinformatic pipeline by the phosphorylated peptide abundances that we observed, highlighting the importance of post-translational modifications in transcription factor regulation. Our multidimensional data analysis workflow allows for a comprehensive characterization of the signaling processes downstream of MC3R activation. It provides insights into protein differential expression, thermal stability, and activities of key transcription factors. All proteomic data generated in this study are publicly available at DOI: 10.6019/PXD039945.

Resistance to ATR Inhibitors Is Mediated by Loss of the Nonsense-Mediated Decay Factor UPF2.

Loss of NMD proteins promotes resistance to ATR inhibitors in gastric cancer cells, which may provide a combination of therapeutic targets and biomarkers to improve the clinical utility of these drugs.

Regional heterogeneity of astrocyte morphogenesis dictated by the formin protein, Daam2, modifies circuit function.

Astrocytes display extraordinary morphological complexity that is essential to support brain circuit development and function. Formin proteins are key regulators of the cytoskeleton; however, their role in astrocyte morphogenesis across diverse brain regions and neural circuits is unknown. Here, we show that loss of the formin protein Daam2 in astrocytes increases morphological complexity in the cortex and olfactory bulb, but elicits opposing effects on astrocytic calcium dynamics. These differential physiological effects result in increased excitatory synaptic activity in the cortex and increased inhibitory synaptic activity in the olfactory bulb, leading to altered olfactory behaviors. Proteomic profiling and immunoprecipitation experiments identify Slc4a4 as a binding partner of Daam2 in the cortex, and combined deletion of Daam2 and Slc4a4 restores the morphological alterations seen in Daam2 mutants. Our results reveal new mechanisms regulating astrocyte morphology and show that congruent changes in astrocyte morphology can differentially influence circuit function.

Boosting Detection of Low-Abundance Proteins in Thermal Proteome Profiling Experiments by Addition of an Isobaric Trigger Channel to TMT Multiplexes.

The study of low-abundance proteins is a challenge to discovery-based proteomics. Mass spectrometry (MS) applications, such as thermal proteome profiling (TPP), face specific challenges in the detection of the whole proteome as a consequence of the use of nondenaturing extraction buffers. TPP is a powerful method for the study of protein thermal stability, but quantitative accuracy is highly dependent on consistent detection. Therefore, TPP can be limited in its amenability to study low-abundance proteins that tend to have stochastic or poor detection by MS. To address this challenge, we incorporated an affinity-purified protein complex sample at submolar concentrations as an isobaric trigger channel into a mutant TPP (mTPP) workflow to provide reproducible detection and quantitation of the low-abundance subunits of the cleavage and polyadenylation factor (CPF) complex. The inclusion of an isobaric protein complex trigger channel increased detection an average of 40× for previously detected subunits and facilitated detection of CPF subunits that were previously below the limit of detection. Importantly, these gains in CPF detection did not cause large changes in melt temperature (Tm) calculations for other unrelated proteins in the samples, with a high positive correlation between Tm estimates in samples with and without isobaric trigger channel addition. Overall, the incorporation of an affinity-purified protein complex as an isobaric trigger channel within a tandem mass tag (TMT) multiplex for mTPP experiments is an effective and reproducible way to gather thermal profiling data on proteins that are not readily detected using the original TPP or mTPP protocols.

Innentitelbild: Die Wechselwirkung mit ribosomalen Proteinen begleitet die Stressinduktion des Wirkstoffkandidaten BOLD‐100/KP1339 im endoplasmatischen Retikulum (Angew. Chem. 10/2021)

Ein Multiomics-Ansatz , der Proteomik, Transkriptomik und Bildgebung umfasst, wurde angewendet, um die Wirkungsweise des Antikrebs-Ruthenium(III)-Wirkstoffkandidaten BOLD-100 umfassend zu untersuchen. In ihrer Zuschrift auf S. 5121 identifizieren Samuel M. Meier-Menches, Christopher Gerner et al. die ribosomalen Proteine RPL10, RPL24 und den Transkriptionsfaktor GTF2I als wahrscheinliche Zielproteine. Sie stehen in funktionellem Zusammenhang mit dem Stress im endoplasmatischen Retikulum, einem der wichtigsten medikamentösen Effekte von BOLD-100.

Interaction with Ribosomal Proteins Accompanies Stress Induction of the Anticancer Metallodrug BOLD-100/KP1339 in the Endoplasmic Reticulum.

The ruthenium‐based anticancer agent BOLD‐100/KP1339 has shown promising results in several in vitro and in vivo tumour models as well as in early clinical trials. However, its mode of action remains to be fully elucidated. Recent evidence identified stress induction in the endoplasmic reticulum (ER) and concomitant down‐modulation of HSPA5 (GRP78) as key drug effects. By exploiting the naturally formed adduct between BOLD‐100 and human serum albumin as an immobilization strategy, we were able to perform target‐profiling experiments that revealed the ribosomal proteins RPL10, RPL24, and the transcription factor GTF2I as potential interactors of this ruthenium(III) anticancer agent. Integrating these findings with proteomic profiling and transcriptomic experiments supported ribosomal disturbance and concomitant induction of ER stress. The formation of polyribosomes and ER swelling of treated cancer cells revealed by TEM validated this finding. Thus, the direct interaction of BOLD‐100 with ribosomal proteins seems to accompany ER stress‐induction and modulation of GRP78 in cancer cells.

Die Wechselwirkung mit ribosomalen Proteinen begleitet die Stressinduktion des Wirkstoffkandidaten BOLD-100/KP1339 im endoplasmatischen Retikulum.

AbstractDer metallhaltige Wirkstoff BOLD‐100/KP1339 zeigte bereits vielversprechende Resultate in verschiedenen In vitro‐ und In vivo‐Tumormodellen sowie in klinischen Studien. Der detaillierte Wirkmechanismus wurde jedoch noch nicht komplett aufgeklärt. Als entscheidende Wirkstoffeffekte kristallisierten sich kürzlich die Stressinduktion im endoplasmatischen Retikulum (ER) und die damit einhergehende Modulierung von HSPA5 (GRP78) heraus. Das spontane und stabile Addukt zwischen BOLD‐100 und menschlichem Serumalbumin wurde als Immobilisierungsstrategie ausgewählt, um einen chemoproteomischen Ansatz auszuführen, der die ribosomalen Proteine RPL10, RPL24 und den Transkriptionsfaktor GTF2I als potentielle Interaktoren dieser Ru(III)‐Verbindung identifizierten. Dieses Ergebnis wurde mit proteomischen und transkriptomischen Profiling‐Experimenten kombiniert, was die Interpretation einer ribosomalen Beeinträchtigung sowie der Induktion von ER‐Stress unterstützte. Die Bildung von Polyribosomen und begleitende ER‐Schwellungen in behandelten Krebszellen wurden zudem durch TEM‐Messungen bestätigt. Somit scheint eine direkte Wechselwirkung von BOLD‐100 mit ribosomalen Proteinen die ER‐Stressinduktion und die Modulierung von GRP78 in Krebszellen zu begleiten.

Rtpca: an R package for differential thermal proximity coaggregation analysis.

SummaryRtpca is an R package implementing methods for inferring protein–protein interactions (PPIs) based on thermal proteome profiling experiments of a single condition or in a differential setting via an approach called thermal proximity coaggregation. It offers user-friendly tools to explore datasets for their PPI predictive performance and easily integrates with available R packages.Availability and implementationRtpca is available from Bioconductor (https://bioconductor.org/packages/Rtpca).Supplementary information Supplementary data are available at Bioinformatics online.

Rhodomyrtone (Rom) is a membrane-active compound

Particularly in Asia medicinal plants with antimicrobial activity are used for therapeutic purpose. One such plant-derived antibiotic is rhodomyrtone (Rom) isolated from Rhodomyrtus tomentosa leaves. Rom shows high antibacterial activity against a wide range of Gram-positive bacteria, however, its mode of action is still unclear. Reporter gene assays and proteomic profiling experiments in Bacillus subtilis indicate that Rom does not address classical antibiotic targets like translation, transcription or DNA replication, but acts at the cytoplasmic membrane. In Staphylococcus aureus, Rom decreases the membrane potential within seconds and at low doses, causes release of ATP and even the excretion of cytoplasmic proteins (ECP), but does not induce pore-formation as for example nisin. Lipid staining revealed that Rom induces local membrane damage. Rom's antimicrobial activity can be antagonized in the presence of a very narrow spectrum of saturated fatty acids (C15:0, C16:0, or C18:0) that most likely contribute to counteract the membrane damage. Gram-negative bacteria are resistant to Rom, presumably due to reduced penetration through the outer membrane and its neutralization by LPS. Rom is cytotoxic for many eukaryotic cells and studies with human erythrocytes showed that Rom induces eryptosis accompanied by erythrocyte shrinkage, cell membrane blebbing, and membrane scrambling with phosphatidylserine translocation to the erythrocyte surface. Rom's distinctive interaction with the cytoplasmic membrane reminds on the amphipathic, alpha-helical peptides, the phenol-soluble modulins (PSMs), and renders Rom an important tool for the investigation of membrane physiology.

Osmoregulation in the Halophilic Bacterium Halomonas elongata: A Case Study for Integrative Systems Biology.

Halophilic bacteria use a variety of osmoregulatory methods, such as the accumulation of one or more compatible solutes. The wide diversity of compounds that can act as compatible solute complicates the task of understanding the different strategies that halophilic bacteria use to cope with salt. This is specially challenging when attempting to go beyond the pathway that produces a certain compatible solute towards an understanding of how the metabolic network as a whole addresses the problem. Metabolic reconstruction based on genomic data together with Flux Balance Analysis (FBA) is a promising tool to gain insight into this problem. However, as more of these reconstructions become available, it becomes clear that processes predicted by genome annotation may not reflect the processes that are active in vivo. As a case in point, E. coli is unable to grow aerobically on citrate in spite of having all the necessary genes to do it. It has also been shown that the realization of this genetic potential into an actual capability to metabolize citrate is an extremely unlikely event under normal evolutionary conditions. Moreover, many marine bacteria seem to have the same pathways to metabolize glucose but each species uses a different one. In this work, a metabolic network inferred from genomic annotation of the halophilic bacterium Halomonas elongata and proteomic profiling experiments are used as a starting point to motivate targeted experiments in order to find out some of the defining features of the osmoregulatory strategies of this bacterium. This new information is then used to refine the network in order to describe the actual capabilities of H. elongata, rather than its genetic potential.

Getting to the Heart of the Matter.

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state.1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression.2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF),3,4 cardiac hypertrophy,5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact, clinically useful: (1) the protein must be easily measurable at a reasonable cost, (2) elevation of the protein would provide information not present in the absence of the protein, and (3) information obtained would guide the medical decisionmaking process.8 Satisfaction of these criteria sets the stage for further analysis in large patient cohort samples, the next step in biomarker validation. Over the past several decades, the field of proteomic research has grown considerably, yet central to most proteomic studies remains the identification of maximal proteins with increased specificity and sensitivity, the identification of protein complexes, and the precise mapping of posttranslational protein modifications.1 Traditionally, gel-based approaches have been used to uncover abnormal protein expression patterns with considerable success. However, some traditional gel-based approaches, such as 2D gel electrophoresis (2DE), tend to suffer from limitations largely related to comprehensive protein identifications, which can be partially addressed using gel-free techniques. With the development of increasingly powerful instruments capable of increasingly accurate analysis and greatly improved separation methods9,10 and experimental design,11 the field of proteomics continues to grow, and innovations in research and development will augment previous proteomic profiling experiments.

Abstract LB-140: Scaling discovery proteomics to large lung cancer cohorts using data independent acquisition

Abstract The success of label free protein quantification relies on larger cohort sizes to accurately profile protein expression patterns enabling specific targets to be translated to confirmatory studies. Thus, proteome profiling experiments must satisfy two primary requirements: maximizing breadth and depth of protein sampling as well as facilitating automated data processing. The approach presented here integrates discovery experiments used to create reference databases and rapid data independent acquisition (DIA) methods to examine large clinical cohorts using < 3 hours of instrument time per individual patient's resected tumor specimen. The combination facilitates exhaustive, automated data mining for protein identification and relative quantitation. Samples used to evaluate the robustness of the study were collected from lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). A sample total of 159 (50 LUSC, 46 LUAD, and 63 adjacent control lung tissues) were analyzed using UHPLC separations coupled to a hybrid quadrupole-orbital ion trap mass spectrometer (QExactive Plus) for DIA. Automated data processing was performed by matching peptides in spectral libraries developed from expression proteomics experiments using pooled tissue proteomes. Data were evaluated to determine differences between LUSC, LUAD, and adjacent control tissue, which were then compared to previous literature to prioritize candidate biomarkers and evaluate the performance of DIA LC-MS/MS for tumor proteome profiling. Hierarchical clustering visualized potential tumor classification schemes based on proteomic phenotypes. In a further study, analysis of cores from a tissue microarray produced quantitative data for > 3,000 proteins per core, indicating that the technology can be applied to minimal amounts of formalin fixed paraffin embedded tumor tissue. Because TMAs are assembled to address specific clinical questions, further analysis of these cohorts is an extremely valuable use for DIA; tumor proteome profiles can be rapidly accumulated and mapped to the clinical variables used to select samples for the TMA. Data independent acquisition provides a method for discovery proteomics that balances a sufficient depth of coverage with the ability to analyze large cohorts of patients (n = 100-400) within 1-2 months on a single instrument. Initial data have been produced for lung squamous cell carcinoma and lung adenocarcinoma, which indicate its utility for assessment of patient groups. Citation Format: Scott Peterman, Bin Fang, Melissa Hoffmann, Amol Prakash, Paul A. Stewart, Richard Liu, Matthew Smith, Joseph Johnson, Steven Eschrich, Guolin Zhang, Eric Haura, John Koomen. Scaling discovery proteomics to large lung cancer cohorts using data independent acquisition. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-140.

Left Ventricular Dysfunction and CXCR3 Ligands in Hypertension: From Animal Experiments to a Population-Based Pilot Study.

Detecting left ventricular (LV) dysfunction at an early stage is key in addressing the heart failure epidemic. In proteome profiling experiments in mice subjected either to aortic banding or sham, the circulating CXCR3 ligands monokine induced by interferon-γ (MIG) and interferon-γ inducible protein 10 (IP10) were 5 to 40 fold up-regulated at eight weeks. We assessed the diagnostic value of circulating NT-pro BNP and CXCR3 ligands (MIG, IP10, Interferon-inducible T-cell alpha chemo-attractant [I–TAC]) in patients with hypertension (≥140/90 mm Hg) associated with subclinical (n = 19) or symptomatic (n = 16) diastolic LV dysfunction on echocardiography and healthy controls. NT–pro BNP, MIG, IP10, I–TAC all increased (p ≤ 0.014) across the categories of worsening left ventricular dysfunction. In patients with symptomatic disease, MIG, IP10, and I–TAC increased 210% (p = 0.015), 140% (p = 0.007) and 120% (p = 0.035) more than NT-pro BNP. The optimal discrimination limits, obtained by maximizing Youden’s index were 246 pmol/L, 65 pg/mL, 93 pg/mL, and 24 pg/mL, respectively. The odds ratios associated with the four biomarkers were significant (p ≤ 0.010), ranging from 4.00 for IP10 to 9.69 for MIG. With adjustment for NT–pro BNP, the CXCR3 ligands retained significance (p ≤ 0.028). Adding optimized thresholds for the CXCR3 ligands to NT–pro BNP enhanced (p ≤ 0.014) the integrated discrimination improvement and the net reclassification improvement. In conclusion, congruent with the concept that inflammation plays a key role in the pathogenesis of LV dysfunction, MIG, IP10 and I–TAC add diagnostic accuracy over and beyond NT–pro BNP.

Target interaction profiling of midostaurin and its metabolites in neoplastic mast cells predicts distinct effects on activation and growth.

Proteomic-based drug testing is an emerging approach to establish the clinical value and anti-neoplastic potential of multikinase inhibitors. The multikinase inhibitor midostaurin (PKC412) is a promising new agent used to treat patients with advanced systemic mastocytosis (SM). We examined the target interaction profiles and the mast cell (MC)-targeting effects of two pharmacologically relevant midostaurin metabolites, CGP52421 and CGP62221. All three compounds, midostaurin and the two metabolites, suppressed IgE-dependent histamine secretion in basophils and MC with reasonable IC(50) values. Midostaurin and CGP62221 also produced growth inhibition and dephosphorylation of KIT in the MC leukemia cell line HMC-1.2, whereas the second metabolite, CGP52421, which accumulates in vivo, showed no substantial effects. Chemical proteomic profiling and drug competition experiments revealed that midostaurin interacts with KIT and several additional kinase targets. The key downstream regulator FES was recognized by midostaurin and CGP62221, but not by CGP52421 in MC lysates, whereas the IgE receptor downstream target SYK was recognized by both metabolites. Together, our data show that the clinically relevant midostaurin metabolite CGP52421 inhibits IgE-dependent histamine release, but is a weak inhibitor of MC proliferation, which may have clinical implications and may explain why mediator-related symptoms improve in SM patients even when disease progression occurs.

A simple dual online ultra-high pressure liquid chromatography system (sDO-UHPLC) for high throughput proteome analysis.

We report a new and simple design of a fully automated dual-online ultra-high pressure liquid chromatography system. The system employs only two nano-volume switching valves (a two-position four port valve and a two-position ten port valve) that direct solvent flows from two binary nano-pumps for parallel operation of two analytical columns and two solid phase extraction (SPE) columns. Despite the simple design, the sDO-UHPLC offers many advantageous features that include high duty cycle, back flushing sample injection for fast and narrow zone sample injection, online desalting, high separation resolution and high intra/inter-column reproducibility. This system was applied to analyze proteome samples not only in high throughput deep proteome profiling experiments but also in high throughput MRM experiments.

Statistical issues in the design and planning of proteomic profiling experiments.

The statistical design of a clinical proteomics experiment is a critical part of well-undertaken investigation. Standard concepts from experimental design such as randomization, replication and blocking should be applied in all experiments, and this is possible when the experimental conditions are well understood by the investigator. The large number of proteins simultaneously considered in proteomic discovery experiments means that determining the number of required replicates to perform a powerful experiment is more complicated than in simple experiments. However, by using information about the nature of an experiment and making simple assumptions this is achievable for a variety of experiments useful for biomarker discovery and initial validation.

Estimating influence of cofragmentation on peptide quantification and identification in iTRAQ experiments by simulating multiplexed spectra.

Isobaric tag-based quantification such as iTRAQ and TMT is a promising approach to mass spectrometry-based quantification in proteomics as it provides wide proteome coverage with greatly increased experimental throughput. However, it is known to suffer from inaccurate quantification and identification of a target peptide due to cofragmentation of multiple peptides, which likely leads to under-estimation of differentially expressed peptides (DEPs). A simple method of filtering out cofragmented spectra with less than 100% precursor isolation purity (PIP) would decrease the coverage of iTRAQ/TMT experiments. In order to estimate the impact of cofragmentation on quantification and identification of iTRAQ-labeled peptide samples, we generated multiplexed spectra with varying degrees of PIP by mixing the two MS/MS spectra of 100% PIP obtained in global proteome profiling experiments on gastric tumor-normal tissue pair proteomes labeled by 4-plex iTRAQ. Despite cofragmentation, the simulation experiments showed that more than 99% of multiplexed spectra with PIP greater than 80% were correctly identified by three different database search engines-MODa, MS-GF+, and Proteome Discoverer. Using the multiplexed spectra that have been correctly identified, we estimated the effect of cofragmentation on peptide quantification. In 74% of the multiplexed spectra, however, the cancer-to-normal expression ratio was compressed, and a fair number of spectra showed the "ratio inflation" phenomenon. On the basis of the estimated distribution of distortions on quantification, we were able to calculate cutoff values for DEP detection from cofragmented spectra, which were corrected according to a specific PIP and probability of type I (or type II) error. When we applied these corrected cutoff values to real cofragmented spectra with PIP larger than or equal to 70%, we were able to identify reliable DEPs by removing about 25% of DEPs, which are highly likely to be false positives. Our experimental results provide useful insight into the effect of cofragmentation on isobaric tag-based quantification methods. The simulation procedure as well as the corrected cutoff calculation method could be adopted for quantifying the effect of cofragmentation and reducing false positives (or false negatives) in the DEP identification with general quantification experiments based on isobaric labeling techniques.

Small Molecule Probe Suitable for In Situ Profiling and Inhibition of Protein Disulfide Isomerase

Proper folding of cellular proteins is assisted by protein disulfide isomerases (PDIs) in the endoplasmic reticulum of mammalian cells. Of the at least 21 PDI family members known in humans, the 57-kDa PDI has been found to be a potential therapeutic target for a variety of human diseases including cancer and neurodegenerative diseases. Consequently, small molecule PDI-targeting inhibitors have been actively pursued in recent years, and thus far, compounds possessing moderate inhibitory activities (IC50 between 0.1 and 100 μM against recombinant PDI) have been discovered. In this article, by using in situ proteome profiling experiments in combination with in vitro PDI enzymatic inhibition assays, we have discovered a phenyl vinyl sulfonate-containing small molecule (P1; shown) as a relatively potent and specific inhibitor of endogenous human PDI in several mammalian cancer cells (e.g., GI50 ∼ 4 μM). It also possesses an IC50 value of 1.7 ± 0.4 μM in an in vitro insulin aggregation assay. Our results indicate P1 is indeed a novel, cell-permeable small molecule PDI inhibitor, and the electrophilic vinyl sulfonate scaffold might serve as a starting point for future development of next-generation PDI inhibitors and probes.

Identification of additional proteins in differential proteomics using protein interaction networks

In this study, we developed a novel computational approach based on protein-protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein-protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest-ranked proteins, beta-arrestin 1, and beta-arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost-effective means to identify additional proteins that remain elusive for current 2D gel-based proteomic profiling techniques.