Statistical protein quantification and significance analysis in label-free LC-MS experiments with complex designs

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Human alveolar macrophages display marked hypo-responsiveness to IFN-γ in both proteomic and gene expression analysis

Alveolar macrophages (AM) perform a primary defense mechanism in the lung through phagocytosis of inhaled particles and microorganisms. AM are known to be relatively immunosuppressive consistent with the aim to limit alveolar inflammation and maintain effective gas exchange in the face of these constant challenges. How AM respond to T cell derived cytokine signals, which are critical to the defense against inhaled pathogens, is less well understood. For example, successful containment of Mycobacterium tuberculosis (Mtb) in lung macrophages is highly dependent on IFN-γ secreted by Th-1 lymphocytes, however, the proteomic IFN-γ response profile in AM remains mostly unknown. In this study, we measured IFN-γ induced protein abundance changes in human AM and autologous blood monocytes (MN). AM cells were activated by IFN-γ stimulation resulting in STAT1 phosphorylation and production of MIG/CXCL9 chemokine. However, the global proteomic response to IFN-γ in AM was dramatically limited in comparison to that of MN (9 AM vs 89 MN differentially abundant proteins). AM hypo-responsiveness was not explained by reduced JAK-STAT1 signaling nor increased SOCS1 expression. These findings suggest that AM have a tightly regulated response to IFN-γ which may prevent excessive pulmonary inflammation but may also provide a niche for the initial survival and growth of Mtb and other intracellular pathogens in the lung.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Human sperm proteome reveals the effect of environmental borne seminal polyaromatic hydrocarbons exposome in etiology of idiopathic male factor infertility

Introduction: Polyaromatic hydrocarbons (PAHs) are considered as redox active environmental toxicants inducing oxidative stress (OS) mediated injury to cells. Oxidative predominance is reported in 30%-80% of idiopathic male infertility (IMI) patients. Hence, this work aims to unravel correlation, if any, between seminal PAH exposome and sperm function in IMI patients through a proteomic approach. Methods: Seminal PAH exposome was analyzed in 43 fertile donors and 60 IMI patients by HPLC and receiver operating characteristic (ROC) curve was applied to find out the cut-off limits. Spermatozoa proteome was analyzed by label free liquid chromatography mass spectroscopy (LC-MS/MS) followed by molecular pathway analysis using bioinformatic tools. Validation of key proteins' expression and protein oxidative modifications were analyzed by western blot. Results and discussion: Of the 16 standards toxic PAH, 13 were detected in semen. Impact of the different PAHs on fertility are Anthracene < benzo (a) pyrene < benzo [b] fluoranthene < Fluoranthene < benzo (a) anthracene <indol (123CD) pyrene < pyrene < naphthalene < dibenzo (AH) anthracene < fluorene < 2bromonaphthalene < chrysene < benzo (GH1) perylene as revealed by ROC Curve analysis (AUC_ROC). Benzo [a] pyrene is invariably present in all infertile patients while naphthalene is present in both groups. Of the total 773 detected proteins (Control: 631 and PAH: 717); 71 were differentially expressed (13 underexpressed, 58 overexpressed) in IMI patients. Enrichment analysis revealed them to be involved in mitochondrial dysfunction and oxidative phosphorylation, DNA damage, Aryl hydrocarbon receptor (AHR) signaling, xenobiotic metabolism and induction of NRF-2 mediated OS response. Increased 4-hydroxynonenal and nitrosylated protein adduct formation, and declined antioxidant defense validates induction of OS. Increased GSH/GSSG ratio in patients may be an adaptive response for PAH metabolism via conjugation as evidenced by over-expression of AHR and Heat shock protein 90 beta (HSP90β) in patients. Seminal PAH concentrations, particularly benzo (a) pyrene can be used as a marker to distinguish IMI from fertile ones with 66.67% sensitivity and 100% specificity (95% confidence interval) along with oxidative protein modification and expression of AHR and HSP90β.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

Mena regulates nesprin-2 to control actin-nuclear lamina associations, trans-nuclear membrane signalling and gene expression

Interactions between cells and the extracellular matrix, mediated by integrin adhesion complexes, play key roles in fundamental cellular processes, including the sensing and transduction of mechanical cues. Here, we investigate systems-level changes in the integrin adhesome in patient-derived cutaneous squamous cell carcinoma cells and identify the actin regulatory protein Mena as a key node in the adhesion complex network. Mena is connected within a subnetwork of actin-binding proteins to the LINC complex component nesprin-2, with which it interacts and co-localises at the nuclear envelope. Moreover, Mena potentiates the interactions of nesprin-2 with the actin cytoskeleton and the nuclear lamina. CRISPR-mediated Mena depletion causes altered nuclear morphology, reduces tyrosine phosphorylation of the nuclear membrane protein emerin and downregulates expression of the immunomodulatory gene PTX3 via the recruitment of its enhancer to the nuclear periphery. We uncover an unexpected role for Mena at the nuclear membrane, where it controls nuclear architecture, chromatin repositioning and gene expression. Our findings identify an adhesion protein that regulates gene transcription via direct signalling across the nuclear envelope.

Proteome-wide structural changes measured with limited proteolysis-mass spectrometry: an advanced protocol for high-throughput applications

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various molecular events, such as post-translational modifications, protein-protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale. In LiP-MS, proteins undergo a brief proteolysis in native conditions followed by complete digestion in denaturing conditions, to generate structurally informative proteolytic fragments that are analyzed by mass spectrometry. We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition-based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on sample number and statistical analysis typically requires ~1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational applications.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

Comparative proteome profiling of seminal components reveal impaired immune cell signalling as paternal contributors in recurrent pregnancy loss patients

PROBLEM

Recurrent pregnancy loss (RPL) is usually evaluated from a women's perspective, however, recent evidence implies involvement of male factors as paternally expressed genes predominate placenta. During fertilization, prior to implantation the immune system purposefully produces early pregnancy factors with potent immunomodulatory properties for adaptation to antigenically dissimilar embryo. Therefore, it is hypothesized that paternal immunological factors play a role in RPL.

METHOD OF STUDY

Comparative proteome profiling (label free liquid chromatography mass spectroscopy: LC-MS/MS) of the seminal extracellular vesicles (SEVs), extracellular vesicle free seminal plasma (EVF-SP) and spermatozoa was carried out in semen of RPL patients (n = 21) and fertile donors (n = 21). This was followed by pathway and protein-protein interaction analysis, and validation of key proteins' expression (western blot).

RESULTS

A total of 68, 28 and 49 differentially expressed proteins in SEVs, EVF-SP and spermatozoa of RPL patients, respectively, were found to be involved in inflammatory response, immune cell signalling and apoptosis. In SEVs, underexpressed GDF-15 and overexpressed C3 imply distorted maternal immune response to paternal antigens leading to impaired decidualization. Dysregulated TGFβ signalling in EVF-SP surmises defective modulation of inflammatory response and induction of immune tolerance to seminal antigens in the female reproductive tract through generation of regulatory T cells. Retained histone variants in spermatozoa construe defective expression of early paternal genes, while underexpressed PTN may inflict defective angiogenesis resulting in expulsion of decidua.

CONCLUSIONS

Impaired modulation of immune response and improper placental development due to altered cytokine levels in seminal components may be the contributing paternal factors in RPL.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

A combined test for feature selection on sparse metaproteomics data-an alternative to missing value imputation

One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely "at random" or "not at random". To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets, as well as simulated data with various scenarios regarding the missingness mechanisms and the nature of the difference of expression (differential intensity or differential presence). Whereas we observed similar performances in terms of prediction on the experimental data set, the feature ranking and selection from various imputation-based methods were strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods, and remains efficient in all simulated scenarios unlike imputation-based feature selection methods.

Comparative analysis of transcriptomic points-of-departure (tPODs) and apical responses in embryo-larval fathead minnows exposed to fluoxetine

Current approaches in chemical hazard assessment face significant challenges because they rely on live animal testing, which is time-consuming, expensive, and ethically questionable. These concerns serve as an impetus to develop new approach methodologies (NAMs) that do not rely on live animal tests. This study explored a molecular benchmark dose (BMD) approach using a 7-day embryo-larval fathead minnow (FHM) assay to derive transcriptomic points-of-departure (tPODs) to predict apical BMDs of fluoxetine (FLX), a highly prescribed and potent selective serotonin reuptake inhibitor frequently detected in surface waters. Fertilized FHM embryos were exposed to graded concentrations of FLX (confirmed at < LOD, 0.19, 0.74, 3.38, 10.2, 47.5 μg/L) for 32 days. Subsets of fish were subjected to omics and locomotor analyses at 7 days post-fertilization (dpf) and to histological and biometric measurements at 32 dpf. Enrichment analyses of transcriptomics and proteomics data revealed significant perturbations in gene sets associated with serotonergic and axonal functions. BMD analysis resulted in tPOD values of 0.56 μg/L (median of the 20 most sensitive gene-level BMDs), 5.0 μg/L (tenth percentile of all gene-level BMDs), 7.51 μg/L (mode of the first peak of all gene-level BMDs), and 5.66 μg/L (pathway-level BMD). These tPODs were protective of locomotor and reduced body weight effects (LOEC of 10.2 μg/L) observed in this study and were reflective of chronic apical BMDs of FLX reported in the literature. Furthermore, the distribution of gene-level BMDs followed a bimodal pattern, revealing disruption of sensitive neurotoxic pathways at low concentrations and metabolic pathway perturbations at higher concentrations. This is one of the first studies to derive protective tPODs for FLX using a short-term embryo assay at a life stage not considered to be a live animal under current legislations.

Proteomic profiling of human amnion for preterm birth biomarker discovery

Spontaneous preterm birth (PTB) complicates about 12% of pregnancies worldwide, remaining the main cause of neonatal morbidity and mortality. Spontaneous preterm birth PTBs is often caused by microbial-induced preterm labor, mediated by an inflammatory process threatening both maternal and newborn health. In search for novel predictive biomarkers of PTB and preterm prelabor rupture of the membranes (pPROM), and to improve understanding of infection related PTB, we performed an untargeted mass spectrometry discovery study on 51 bioptic mid zone amnion samples from premature babies. A total of 6352 proteins were identified. Bioinformatics analyses revealed a ranked core of 159 proteins maximizing the discrimination between the selected clinical stratification groups allowing to distinguish conditions of absent (FIR 0) from maximal Fetal Inflammatory Response (FIR 3) stratified in function of Maternal Inflammatory Response (MIR) grade. Matrix metallopeptidase-9 (MMP-9) was the top differentially expressed protein. Gene Ontology enrichment analysis of the core proteins showed significant changes in the biological pathways associated to inflammation and regulation of immune and infection response. Data suggest that the conditions determining PTB would be a transversal event, secondary to the maternal inflammatory response causing a breakdown in fetal-maternal tolerance, with fetal inflammation being more severe than maternal one. We also highlight matrix metallopeptidase-9 as a potential predictive biomarker of PTB that can be assayed in the maternal serum, for future investigation.

Heterogeneity in proline hydroxylation of fibrillar collagens observed by mass spectrometry

Collagen is the major protein in the extracellular matrix and plays vital roles in tissue development and function. Collagen is also one of the most processed proteins in its biosynthesis. The most prominent post-translational modification (PTM) of collagen is the hydroxylation of Pro residues in the Y-position of the characteristic (Gly-Xaa-Yaa) repeating amino acid sequence of a collagen triple helix. Recent studies using mass spectrometry (MS) and tandem MS sequencing (MS/MS) have revealed unexpected hydroxylation of Pro residues in the X-positions (X-Hyp). The newly identified X-Hyp residues appear to be highly heterogeneous in location and percent occupancy. In order to understand the dynamic nature of the new X-Hyps and their potential impact on applications of MS and MS/MS for collagen research, we sampled four different collagen samples using standard MS and MS/MS techniques. We found considerable variations in the degree of PTMs of the same collagen from different organisms and/or tissues. The rat tail tendon type I collagen is particularly variable in terms of both over-hydroxylation of Pro in the X-position and under-hydroxylation of Pro in the Y-position. In contrast, only a few unexpected PTMs in collagens type I and type III from human placenta were observed. Some observations are not reproducible between different sequencing efforts of the same sample, presumably due to a low population and/or the unpredictable nature of the ionization process. Additionally, despite the heterogeneous preparation and sourcing, collagen samples from commercial sources do not show elevated variations in PTMs compared to samples prepared from a single tissue and/or organism. These findings will contribute to the growing body of information regarding the PTMs of collagen by MS technology, and culminate to a more comprehensive understanding of the extent and the functional roles of the PTMs of collagen.

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

Proteomic Alterations in Salivary Exosomes Derived from Human Papillomavirus-Driven Oropharyngeal Cancer

BACKGROUND

Increasing evidence supports the notion that human papillomavirus (HPV) DNA integration onto the human genome can influence and alter the molecular cargo in the exosomes derived from head and neck cancer cells. However, the molecular cargo of salivary exosomes derived from HPV-driven oropharyngeal cancer (HPV-driven OPC) remains unelucidated.

METHODS AND MATERIALS

Salivary exosomes morphology and molecular characterizations were examined using the nanoparticle tracking (NTA), western blot analysis, transmission electron microscopy (TEM) and mass spectrometry analysis.

RESULTS

We report that HPV16 DNA was detected (80%) in isolated salivary exosomes of HPV-driven OPC patients. Importantly, we demonstrate elevated protein levels of six main glycolytic enzymes [i.e., aldolase (ALDOA), glyceraldehye-3-phosphate dehydrogenase (GAPDH), lactate dehydrogenase A/B (LDHA and LDHB), phosphoglycerate kinase 1 (PGK1) and pyruvate kinase M1/2 (PKM)] in isolated salivary exosomes of HPV-driven OPC patients, suggesting a novel mechanism underlying the potential role of salivary exosomes in mediating the reciprocal interplay between glucose metabolism and HPV-driven OPC.

CONCLUSION

Our data demonstrate the potential diagnostic value of HPV16 DNA and glycolytic enzymes in salivary exosomes in discriminating healthy controls from HPV-driven OPC patients, thereby opening new avenues in the future for clinical translation studies.

Cell-blocks and other ancillary studies (including molecular genetic tests and proteomics)

Many types of elective ancillary tests may be required to support the cytopathologic interpretations. Most of these tests can be performed on cell-blocks of different cytology specimens. The cell-block sections can be used for almost any special stains including various histochemistry stains and for special stains for different microorganisms including fungi, Pneumocystis jirovecii (carinii), and various organisms including acid-fast organisms similar to the surgical biopsy specimens. Similarly, in addition to immunochemistry, different molecular tests can be performed on cell-blocks. Molecular tests broadly can be divided into two main types Molecular genetic tests and Proteomics.

Benchmarking Quantitative Performance in Label-Free Proteomics

Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number (n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Differential expression of the five redox complexes in the retinal mitochondria or rod outer segment disks is consistent with their different functionality

PURPOSE

The retinal rod outer segment (OS) disk membranes, devoid of mitochondria, conducts oxidative phosphorylation (OxPhos). This study aimed at identifying which proteins expressed in the retinal rod OS disks determined the considerable adenosine-5'-triphosphate production and oxygen consumption observed in comparison with retinal mitochondria.

PROCEDURES

Characterization was conducted by immunogold transmission electron microscopy on retinal sections. OxPhos was studied by oximetry and luminometry. The proteomes of OS disks and mitochondria purified from bovine retinas were studied by mass spectrometry. Statistical and bioinformatic analyses were conducted by univariate, multivariate, and machine learning methods.

RESULTS

Weighted gene coexpression network analysis identified two protein expression profile modules functionally associated with either retinal mitochondria or disk samples, in function of a strikingly different ability of each sample to utilized diverse substrate for F1Fo-ATP synthase. The OS disk proteins correlated better than mitochondria with the tricarboxylic acids cycle and OxPhos proteins.

CONCLUSIONS

The differential enrichment of the expression profile of the OxPhos proteins in the disks versus mitochondria suggests that these proteins may represent a true proteome component of the former, with different functionality. These findings may shed new light on the pathogenesis of rod-driven retinal degenerative diseases.

Global site-specific neddylation profiling reveals that NEDDylated cofilin regulates actin dynamics

Neddylation is the post-translational protein modification most closely related to ubiquitination. Whereas the ubiquitin-like protein NEDD8 is well studied for its role in activating cullin-RING E3 ubiquitin ligases, little is known about other substrates. We developed serial NEDD8-ubiquitin substrate profiling (sNUSP), a method that employs NEDD8 R74K knock-in HEK293 cells, allowing discrimination of endogenous NEDD8- and ubiquitin-modification sites by MS after Lys-C digestion and K-εGG-peptide enrichment. Using sNUSP, we identified 607 neddylation sites dynamically regulated by the neddylation inhibitor MLN4924 and the de-neddylating enzyme NEDP1, implying that many non-cullin proteins are neddylated. Among the candidates, we characterized lysine 112 of the actin regulator cofilin as a novel neddylation event. Global inhibition of neddylation in developing neurons leads to cytoskeletal defects, altered actin dynamics and neurite growth impairments, whereas site-specific neddylation of cofilin at K112 regulates neurite outgrowth, suggesting that cofilin neddylation contributes to the regulation of neuronal actin organization.

Review of Batch Effects Prevention, Diagnostics, and Correction Approaches

Systematic technical variation in high-throughput studies consisting of the serial measurement of large sample cohorts is known as batch effects. Batch effects reduce the sensitivity of biological signal extraction and can cause significant artifacts. The systematic bias in the data caused by batch effects is more common in studies in which logistical considerations restrict the number of samples that can be prepared or profiled in a single experiment, thus necessitating the arrangement of subsets of study samples in batches. To mitigate the negative impact of batch effects, statistical approaches for batch correction are used at the stage of experimental design and data processing. Whereas in genomics batch effects and possible remedies have been extensively discussed, they are a relatively new challenge in proteomics because methods with sufficient throughput to systematically measure through large sample cohorts have only recently become available. Here we provide general recommendations to mitigate batch effects: we discuss the design of large-scale proteomic studies, review the most commonly used tools for batch effect correction and overview their application in proteomics.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Effect of Antioxidant Supplementation on the Sperm Proteome of Idiopathic Infertile Men

Antioxidant supplementation in idiopathic male infertility has a beneficial effect on semen parameters. However, the molecular mechanism behind this effect has not been reported. The objective of this study was to evaluate the sperm proteome of idiopathic infertile men pre- and post-antioxidant supplementation. Idiopathic infertile men were provided with oral antioxidant supplementation once daily for a period of 6 months. Of the 379 differentially expressed proteins (DEPs) between pre- and post-antioxidant treatment patients, the majority of the proteins (n = 274) were overexpressed following antioxidant treatment. Bioinformatic analysis revealed the activation of oxidative phosphorylation pathway and upregulation of key proteins involved in spermatogenesis, sperm maturation, binding of sperm, fertilization and normal reproductive function. In addition, the transcriptional factors associated with antioxidant defense system (PPARGC1A) and free radical scavenging (NFE2L2) were predicted to be functionally activated post-treatment. Key DEPs, namely, NDUFS1, CCT3, PRKARA1 and SPA17 validated by Western blot showed significant overexpression post-treatment. Our novel proteomic findings suggest that antioxidant supplementation in idiopathic infertile men improves sperm function at the molecular level by modulating proteins involved in CREM signaling, mitochondrial function and protein oxidation. Further, activation of TRiC complex helped in nuclear compaction, maintenance of telomere length, flagella function, and expression of zona pellucida receptors for sperm-oocyte interaction.

Changes in the microsomal proteome of tomato fruit during ripening

The variations in the membrane proteome of tomato fruit pericarp during ripening have been investigated by mass spectrometry-based label-free proteomics. Mature green (MG30) and red ripe (R45) stages were chosen because they are pivotal in the ripening process: MG30 corresponds to the end of cellular expansion, when fruit growth has stopped and fruit starts ripening, whereas R45 corresponds to the mature fruit. Protein patterns were markedly different: among the 1315 proteins identified with at least two unique peptides, 145 significantly varied in abundance in the process of fruit ripening. The subcellular and biochemical fractionation resulted in GO term enrichment for organelle proteins in our dataset, and allowed the detection of low-abundance proteins that were not detected in previous proteomic studies on tomato fruits. Functional annotation showed that the largest proportion of identified proteins were involved in cell wall metabolism, vesicle-mediated transport, hormone biosynthesis, secondary metabolism, lipid metabolism, protein synthesis and degradation, carbohydrate metabolic processes, signalling and response to stress.

Hypoplastic left heart syndrome: From bedside to bench and back

Hypoplastic Left Heart Syndrome (HLHS) is a complex Congenital Heart Disease (CHD) that was almost universally fatal until the advent of the Norwood operation in 1981. Children with HLHS who largely succumbed to the disease within the first year of life, are now surviving to adulthood. However, this survival is associated with multiple comorbidities and HLHS infants have a higher mortality rate as compared to other non-HLHS single ventricle patients. In this review we (a) discuss current clinical challenges associated in the care of HLHS patients, (b) explore the use of systems biology in understanding the molecular framework of this disease, (c) evaluate induced pluripotent stem cells as a translational model to understand molecular mechanisms and manipulate them to improve outcomes, and (d) investigate cell therapy, gene therapy, and tissue engineering as a potential tool to regenerate hypoplastic cardiac structures and improve outcomes.

Bayesian Confidence Intervals for Multiplexed Proteomics Integrate Ion-statistics with Peptide Quantification Concordance

Multiplexed proteomics has emerged as a powerful tool to measure relative protein expression levels across multiple conditions. The relative protein abundances are inferred by comparing the signals generated by isobaric tags, which encode the samples' origins. Intuitively, the trust associated with a protein measurement depends on the similarity of ratios from the protein's peptides and the signal-strength of these measurements. However, typically the average peptide ratio is reported as the estimate of relative protein abundance, which is only the most likely ratio with a very naive model. Moreover, there is no sense on the confidence in these measurements. Here, we present a mathematically rigorous approach that integrates peptide signal strengths and peptide-measurement agreement into an estimation of the true protein ratio and the associated confidence (BACIQ). The main advantages of BACIQ are: (1) It removes the need to threshold reported peptide signal based on an arbitrary cut-off, thereby reporting more measurements from a given experiment; (2) Confidence can be assigned without replicates; (3) For repeated experiments BACIQ provides confidence intervals for the union, not the intersection, of quantified proteins; (4) For repeated experiments, BACIQ confidence intervals are more predictive than confidence intervals based on protein measurement agreement. To demonstrate the power of BACIQ we reanalyzed previously published data on subcellular protein movement on treatment with an Exportin-1 inhibiting drug. We detect ∼2× more highly significant movers, down to subcellular localization changes of ∼1%. Thus, our method drastically increases the value obtainable from quantitative proteomics experiments, helping researchers to interpret their data and prioritize resources. To make our approach easily accessible we distribute it via a Python/Stan package.

Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics

In any analytical discipline, data analysis reproducibility is closely interlinked with data quality. In this book chapter focused on mass spectrometry-based proteomics approaches, we introduce how both data analysis reproducibility and data quality can influence each other and how data quality and data analysis designs can be used to increase robustness and improve reproducibility. We first introduce methods and concepts to design and maintain robust data analysis pipelines such that reproducibility can be increased in parallel. The technical aspects related to data analysis reproducibility are challenging, and current ways to increase the overall robustness are multifaceted. Software containerization and cloud infrastructures play an important part.We will also show how quality control (QC) and quality assessment (QA) approaches can be used to spot analytical issues, reduce the experimental variability, and increase confidence in the analytical results of (clinical) proteomics studies, since experimental variability plays a substantial role in analysis reproducibility. Therefore, we give an overview on existing solutions for QC/QA, including different quality metrics, and methods for longitudinal monitoring. The efficient use of both types of approaches undoubtedly provides a way to improve the experimental reliability, reproducibility, and level of consistency in proteomics analytical measurements.

Multi-Omics Characterization of the Spontaneous Mesenchymal-Epithelial Transition in the PMC42 Breast Cancer Cell Lines

Epithelial-mesenchymal plasticity (EMP), encompassing epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET), are considered critical events for cancer metastasis. We investigated chromosomal heterogeneity and chromosomal instability (CIN) profiles of two sister PMC42 breast cancer (BC) cell lines to assess the relationship between their karyotypes and EMP phenotypic plasticity. Karyotyping by GTG banding and exome sequencing were aligned with SWATH quantitative proteomics and existing RNA-sequencing data from the two PMC42 cell lines; the mesenchymal, parental PMC42-ET cell line and the spontaneously epithelially shifted PMC42-LA daughter cell line. These morphologically distinct PMC42 cell lines were also compared with five other BC cell lines (MDA-MB-231, SUM-159, T47D, MCF-7 and MDA-MB-468) for their expression of EMP and cell surface markers, and stemness and metabolic profiles. The findings suggest that the epithelially shifted cell line has a significantly altered ploidy of chromosomes 3 and 13, which is reflected in their transcriptomic and proteomic expression profiles. Loss of the TGFβR2 gene from chromosome 3 in the epithelial daughter cell line inhibits its EMT induction by TGF-β stimulus. Thus, integrative 'omics' characterization established that the PMC42 system is a relevant MET model and provides insights into the regulation of phenotypic plasticity in breast cancer.

Structural and functional differentiation of the light-harvesting protein Lhcb4 during land plant diversification

About 475 million years ago, plants originated from an ancestral green alga and evolved first as non-vascular and later as vascular plants, becoming the primary producers of biomass on lands. During that time, the light-harvesting complex II (LHCII), responsible for sunlight absorption and excitation energy transfer to the photosystem II (PSII) core, underwent extensive differentiation. Lhcb4 is an ancestral LHCII that, in flowering plants, differentiated into up to three isoforms, Lhcb4.1, Lhcb4.2 and Lhcb4.3. The pivotal position of Lhcb4 in the PSII-LHCII supercomplex (PSII-LHCIIsc) allows functioning as linker for either S- or M-trimers of LHCII to the PSII core. The increased accumulation of Lhcb4.3 observed in PSII-LHCIIsc of plants acclimated to moderate and high light intensities induced us to investigate, whether this isoform has a preferential localization in a specific PSII-LHCIIsc conformation that might explain its light-dependent accumulation. In this work, by combining an improved method for separation of different forms of PSII-LHCIIsc from thylakoids of Pisum sativum L. grown at increasing irradiances with quantitative proteomics, we assessed that Lhcb4.3 is abundant in PSII-LHCIIsc of type C₂ S₂ , and, interestingly, similar results were found for the PsbR subunit. Phylogenetic comparative analysis on different taxa of the Viridiplantae lineage and structural modeling further pointed out to an effect of the evolution of different Lhcb4 isoforms on the light-dependent modulation of the PSII-LHCIIsc organization. This information provides new insight on the properties of the Lhcb4 and its isoforms and their role on the structure, function and regulation of PSII.

MetaMSD: meta analysis for mass spectrometry data

Mass spectrometry-based proteomics facilitate disease understanding by providing protein abundance information about disease progression. For the same type of disease studies, multiple mass spectrometry datasets may be generated. Integrating multiple mass spectrometry datasets can provide valuable information that a single dataset analysis cannot provide. In this article, we introduce a meta-analysis software, MetaMSD (Meta Analysis for Mass Spectrometry Data) that is specifically designed for mass spectrometry data. Using Stouffer’s or Pearson’s test, MetaMSD detects significantly more differential proteins than the analysis based on the single best experiment. We demonstrate the performance of MetaMSD using simulated data, urinary proteomic data of kidney transplant patients, and breast cancer proteomic data. Noting the common practice of performing a pilot study prior to a main study, this software will help proteomics researchers fully utilize the benefit of multiple studies (or datasets), thus optimizing biomarker discovery. MetaMSD is a command line tool that automatically outputs various graphs and differential proteins with confidence scores. It is implemented in R and is freely available for public use at https://github.com/soyoungryu/MetaMSD. The user manual and data are available at the site. The user manual is written in such a way that scientists who are not familiar with R software can use MetaMSD.

Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry: Technical and Statistical Considerations

INTRODUCTION

The quantitative measurements based on liquid chromatography (LC) coupled with mass spectrometry (MS) often suffer from the problem of missing values and data heterogeneity from technical variability. We considered a proteomics data set generated from human kidney biopsy material to investigate the technical effects of sample preparation and the quantitative MS.

METHODS

We studied the effect of tissue storage methods (TSMs) and tissue extraction methods (TEMs) on data analysis. There are two TSMs: frozen (FR) and FFPE (formalin-fixed paraffin embedded); and three TEMs: MAX, TX followed by MAX and SDS followed by MAX. We assessed the impact of different strategies to analyze the data while considering heterogeneity and MVs. We have used analysis of variance (ANOVA) model to study the effects due to various sources of variability.

RESULTS AND CONCLUSION

We found that the FFPE TSM is better than the FR TSM. We also found that the one-step TEM (MAX) is better than those of two-steps TEMs. Furthermore, we found the imputation method is a better approach than excluding the proteins with MVs or using unbalanced design.

Robust determination of differential abundance in shotgun proteomics using nonparametric statistics

Label-free shotgun mass spectrometry enables the detection of significant changes in protein abundance between different conditions. Due to often limited cohort sizes or replication, large ratios of potential protein markers to number of samples, as well as multiple null measurements pose important technical challenges to conventional parametric models. From a statistical perspective, a scenario similar to that of unlabeled proteomics is encountered in genomics when looking for differentially expressed genes. Still, the difficulty of detecting a large fraction of the true positives without a high false discovery rate is arguably greater in proteomics due to even smaller sample sizes and peptide-to-peptide variability in detectability. These constraints argue for nonparametric (or distribution-free) tests on normalized peptide values, thus minimizing the number of free parameters, as well as for measuring significance with permutation testing. We propose such a procedure with a class-based statistic, no parametric assumptions, and no parameters to select other than a nominal false discovery rate. Our method was tested on a new dataset which is available via ProteomeXchange with identifier PXD006447. The dataset was prepared using a standard proteolytic digest of a human protein mixture at 1.5-fold to 3-fold protein concentration changes and diluted into a constant background of yeast proteins. We demonstrate its superiority relative to other approaches in terms of the realized sensitivity and realized false discovery rates determined by ground truth, and recommend it for detecting differentially abundant proteins from MS data.

The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments

An idealized version of a label-free discovery mass spectrometry proteomics experiment would provide absolute abundance measurements for a whole proteome, across varying conditions. Unfortunately, this ideal is not realized. Measurements are made on peptides requiring an inferential step to obtain protein level estimates. The inference is complicated by experimental factors that necessitate relative abundance estimation and result in widespread non-ignorable missing data. Relative abundance on the log scale takes the form of parameter contrasts. In a complete-case analysis, contrast estimates may be biased by missing data and a substantial amount of useful information will often go unused. To avoid problems with missing data, many analysts have turned to single imputation solutions. Unfortunately, these methods often create further difficulties by hiding inestimable contrasts, preventing the recovery of interblock information and failing to account for imputation uncertainty. To mitigate many of the problems caused by missing values, we propose the use of a Bayesian selection model. Our model is tested on simulated data, real data with simulated missing values, and on a ground truth dilution experiment where all of the true relative changes are known. The analysis suggests that our model, compared with various imputation strategies and complete-case analyses, can increase accuracy and provide substantial improvements to interval coverage.

Quantitative glycomics using liquid phase separations coupled to mass spectrometry

Post-translational modification of proteins by the attachment of glycans is governed by a variety of highly specific enzymes and is associated with fundamental impacts on the parent protein's physical, chemical and biological properties. The inherent connection between cellular physiology and specific glycosylation patterns has been shown to offer potential for diagnostic and prognostic monitoring of altered glycosylation in the disease state. Conversely, glycoprotein based biopharmaceuticals have emerged as dominant therapeutic strategies in the treatment of intricate diseases. Glycosylation present on these biopharmaceuticals represents a major critical quality attribute with impacts on both pharmacokinetics and pharmacodynamics. The structural variety of glycans, based upon their non-template driven assembly, poses a significant analytical challenge for both qualitative and quantitative analysis. Labile monosaccharide constituents, isomeric species and often low sample availability from biological sources necessitates meticulous sample handling, ultra-high-resolution analytical separation and sensitive detection techniques, respectively. In this article a critical review of analytical quantitation approaches using liquid phase separations coupled to mass spectrometry for released glycans of biopharmaceutical and biomedical significance is presented. Considerations associated with sample derivatisation strategies, ionisation, relative quantitation through isotopic as well as isobaric labelling, metabolic/enzymatic incorporation and targeted analysis are all thoroughly discussed.

A Methodological Assessment and Characterization of Genetically-Driven Variation in Three Human Phosphoproteomes

Phosphorylation of proteins on serine, threonine, and tyrosine residues is a ubiquitous post-translational modification that plays a key part of essentially every cell signaling process. It is reasonable to assume that inter-individual variation in protein phosphorylation may underlie phenotypic differences, as has been observed for practically any other molecular regulatory phenotype. However, we do not know much about the extent of inter-individual variation in phosphorylation because it is quite challenging to perform a quantitative high throughput study to assess inter-individual variation in any post-translational modification. To test our ability to address this challenge with SILAC-based mass spectrometry, we quantified phosphorylation levels for three genotyped human cell lines within a nested experimental framework, and found that genetic background is the primary determinant of phosphoproteome variation. We uncovered multiple functional, biophysical, and genetic associations with germline driven phosphopeptide variation. Variants affecting protein levels or structure were among these associations, with the latter presenting, on average, a stronger effect. Interestingly, we found evidence that is consistent with a phosphopeptide variability buffering effect endowed from properties enriched within longer proteins. Because the small sample size in this 'pilot' study may limit the applicability of our genetic observations, we also undertook a thorough technical assessment of our experimental workflow to aid further efforts. Taken together, these results provide the foundation for future work to characterize inter-individual variation in post-translational modification levels and reveal novel insights into the nature of inter-individual variation in phosphorylation.

Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer

In the United States alone one in five newly diagnosed cancers in men are prostate carcinomas (PCa). Androgen receptor (AR) status and the PI3K-AKT-mTOR signal transduction pathway are critical in PCa. After initial response to single drugs targeting these pathways resistance often emerges, indicating the need for combination therapy. Here, we address the question of efficacy of drug combinations and development of resistance mechanisms to targeted therapy by a systems pharmacology approach. We combine targeted perturbation with detailed observation of the molecular response by mass spectrometry. We hypothesize that the molecular short-term (24 h) response reveals details of how PCa cells adapt to counter the anti-proliferative drug effect. With focus on six drugs currently used in PCa treatment or targeting the PI3K-AKT-mTOR signal transduction pathway, we perturbed the LNCaP clone FGC cell line by a total of 21 treatment conditions using single and paired drug combinations. The molecular response was analyzed by the mass spectrometric quantification of 52 proteins. Analysis of the data revealed a pattern of strong responders, i.e., proteins that were consistently downregulated or upregulated across many of the perturbation conditions. The downregulated proteins, HN1, PAK1, and SPAG5, are potential early indicators of drug efficacy and point to previously less well-characterized response pathways in PCa cells. Some of the upregulated proteins such as 14-3-3 proteins and KLK2 may be useful early markers of adaptive response and indicate potential resistance pathways targetable as part of combination therapy to overcome drug resistance. The potential of 14-3-3ζ (YWHAZ) as a target is underscored by the independent observation, based on cancer genomics of surgical specimens, that its DNA copy number and transcript levels tend to increase with PCa disease progression. The combination of systematic drug perturbation combined with detailed observation of short-term molecular response using mass spectrometry is a potentially powerful tool to discover response markers and anti-resistance targets.

Metastatic prostate cancer is often treated with pharmacological agents to prevent the tumor from expanding; however, despite advances in drug development patients often die of the disease. An international research team lead by Ruedi Aebersold (ETH Zürich, Switzerland) and Chris Sander (Dana Faber Cancer Institute, Boston, USA) asked how prostate cancer cells adapt to pharmacological treatment on the molecular protein level and find a general response in their prostate cancer model. Next, they asked if similar changes are found in prostate cancer patients. Indeed, the same proteins upregulated in prostate cancer models are also upregulated in prostate cancer patients. Immediately, this has implications for patient treatment stratification and opens new avenues for drug developments in metastatic prostate cancer.

Comprehensive proteome analysis of nasal lavage samples after controlled exposure to welding nanoparticles shows an induced acute phase and a nuclear receptor, LXR/RXR, activation that influence the status of the extracellular matrix

BACKGROUND

Epidemiological studies have shown that many welders experience respiratory symptoms. During the welding process a large number of airborne nanosized particles are generated, which might be inhaled and deposited in the respiratory tract. Knowledge of the underlying mechanisms behind observed symptoms is still partly lacking, although inflammation is suggested to play a central role. The aim of this study was to investigate the effects of welding fume particle exposure on the proteome expression level in welders suffering from respiratory symptoms, and changes in protein mediators in nasal lavage samples were analyzed. Such mediators will be helpful to clarify the pathomechanisms behind welding fume particle-induced effects.

METHODS

In an exposure chamber, 11 welders with work-related symptoms in the lower airways during the last month were exposed to mild-steel welding fume particles (1 mg/m3) and to filtered air, respectively, in a double-blind manner. Nasal lavage samples were collected before, immediately after, and the day after exposure. The proteins in the nasal lavage were analyzed with two different mass spectrometry approaches, label-free discovery shotgun LC-MS/MS and a targeted selected reaction monitoring LC-MS/MS analyzing 130 proteins and four in vivo peptide degradation products.

RESULTS

The analysis revealed 30 significantly changed proteins that were associated with two main pathways; activation of acute phase response signaling and activation of LXR/RXR, which is a nuclear receptor family involved in lipid signaling. Connective tissue proteins and proteins controlling the degradation of such tissues, including two different matrix metalloprotease proteins, MMP8 and MMP9, were among the significantly changed enzymes and were identified as important key players in the pathways.

CONCLUSION

Exposure to mild-steel welding fume particles causes measurable changes on the proteome level in nasal lavage matrix in exposed welders, although no clinical symptoms were manifested. The results suggested that the exposure causes an immediate effect on the proteome level involving acute phase proteins and mediators regulating lipid signaling. Proteases involved in maintaining the balance between the formation and degradation of extracellular matrix proteins are important key proteins in the induced effects.

Inositol polyphosphate multikinase regulation of Trypanosoma brucei life stage development

The regulation of Trypanosoma brucei life stage development remains unclear. Inositol polyphosphate multikinase regulates the development of mammalian bloodforms to insect stages that normally develop in flies. Specific inositol phosphates, perhaps as second messengers, interact with proteins of the regulatory network that controls development.

Many cellular processes change during the Trypanosoma brucei life cycle as this parasite alternates between the mammalian host and tsetse fly vector. We show that the inositol phosphate pathway helps regulate these developmental changes. Knockdown of inositol polyphosphate multikinase (IPMK), which phosphorylates Ins(1,4,5)P3 and Ins(1,3,4,5)P4, resulted in changes in bloodstream forms that are characteristic of insect stage procyclic forms. These changes include expression of the procyclic surface coat, up-regulation of RNA-binding proteins that we show to regulate stage-specific transcripts, and activation of oxidative phosphorylation with increased ATP production in bloodstream forms. These changes were accompanied by development of procyclic morphology, which also occurred by the expression of a catalytically inactive IPMK, implying that regulation of these processes entails IPMK activity. Proteins involved in signaling, protein synthesis and turnover, and metabolism were affinity-enriched with the IPMK substrate or product. Developmental changes associated with IPMK knockdown or catalytic inactivation reflected processes that are enriched with inositol phosphates, and chemical and genetic perturbation of these processes affected T. brucei development. Hence, IPMK helps regulate T. brucei development, perhaps by affecting inositol phosphate interactions with proteins of the regulatory network that controls energy metabolism and development.

Proteome of Bovine Mitochondria and Rod Outer Segment Disks: Commonalities and Differences

The retinal rod outer segment (OS) is a stack of disks surrounded by the plasma membrane, housing proteins related to phototransduction, as well as mitochondrial proteins involved in oxidative phosphorylation (OxPhos). This prompted us to compare the proteome of bovine OS disks and mitochondria to assess the significant top gene signatures of each sample. The two proteomes, obtained by LTQ-Orbitrap Velos mass spectrometry, were compared by statistical analyses. In total, 4139 proteins were identified, 2045 of which overlapping in the two sets. Nonhierarchical Spearman's correlogram revealed that the groups were clearly discriminated. Partial least square discriminant plus support vector machine analysis identified the major discriminative proteins, implied in phototransduction and lipid metabolism, respectively. Gene Ontology analysis identified top gene signatures of the disk proteome, enriched in vesiculation, glycolysis, and OxPhos proteins. The tricarboxylic acid cycle and the electron transport proteins were similarly enriched in the two samples, but the latter was up regulated in disks. Data suggest that the mitochondrial OxPhos proteins may represent a true OS proteome component, outside the mitochondrion. This knowledge may help the scientific community in the further studies of retinal physiology and pathology.

Fluoxetine, not donepezil, reverses anhedonia, cognitive dysfunctions and hippocampal proteome changes during repeated social defeat exposure

While anhedonia is considered a core symptom of major depressive disorder (MDD), less attention has been paid to cognitive dysfunctions. We evaluated the behavioural and molecular effects of a selective serotonin re-uptake inhibitor (SSRI, fluoxetine) and an acetylcholinesterase inhibitor (AChEI, donepezil) on emotional-cognitive endophenotypes of depression and the hippocampal proteome. A chronic social defeat (SD) procedure was followed up by "reminder" sessions of direct and indirect SD. Anhedonia-related behaviour was assessed longitudinally by intracranial self-stimulation (ICSS). Cognitive dysfunction was analysed by an object recognition test (ORT) and extinction of fear memory. Tandem mass spectrometry (MS^E) and protein-protein-interaction (PPI) network modelling were used to characterise the underlying biological processes of SD and SSRI/AChEI treatment. Independent selected reaction monitoring (SRM) was conducted for molecular validation. Repeated SD resulted in a stable increase of anhedonia-like behaviour as measured by ICSS. Fluoxetine treatment reversed this phenotype, whereas donepezil showed no effect. Fluoxetine improved recognition memory and inhibitory learning in a stressor-related context, whereas donepezil only improved fear extinction. MS^E and PPI network analysis highlighted functional SD stress-related hippocampal proteome changes including reduced glutamatergic neurotransmission and learning processes, which were reversed by fluoxetine, but not by donepezil. SRM validation of molecular key players involved in these pathways confirmed the hypothesis that fluoxetine acts via increased AMPA receptor signalling and Ca²⁺-mediated neuroplasticity in the amelioration of stress-impaired reward processing and memory consolidation. Our study highlights molecular mediators of SD stress reversed by SSRI treatment, identifying potential viable future targets to improve cognitive dysfunctions in MDD patients.

Identification and Validation of a Salivary Protein Panel to Detect Heart Failure Early

BACKGROUND

Over 26 million people suffer from heart failure (HF) globally. Current diagnosis of HF relies on clinical evaluation, blood assays and imaging techniques. Our aim is to develop a diagnostic assay to detect HF in at risk individuals within the community using human saliva as a medium, potentially leading to a simple, safe early warning system.

METHODS

Saliva samples were collected from healthy controls (n=36) and HF patients (n=75). Salivary proteome profiles were analysed by Sequential Window Acquisition of All Theoretical fragment ion spectra - Mass Spectrometry (SWATH-MS). A total of 738 proteins were quantified and 177 proteins demonstrated significant differences between HF patients and healthy controls. Candidate biomarkers were chosen based on their abundance and difference between the two cohorts. A multi-protein panel was developed using logistic regression analysis. The diagnostic performance of the multi-protein panel was assessed using receiver operative characteristic curves. The candidate proteins were further confirmed, using western blot analysis, and validated technically, using an independent biological cohort.

RESULTS

A group of six proteins were chosen in the discovery phase as potential candidates based on their differences in the abundance between the two cohorts. During the validation phase, two of the proteins were not detected with western blotting and as such were removed. The final panel consists of four proteins with sensitivity of 83.3%, specificity of 62.5% with an area under ROC curve of 0.78 in discriminating healthy controls from NYHA class I/II HF patients, and was validated in a second independent cohort study.

CONCLUSION

Analysis of salivary proteome using SWATH-MS revealed novel HF-specific protein candidates yielding high diagnostic performance. A multi-centre longitudinal clinical trial will be the next step before clinical implementation of this panel.

A brain proteomic investigation of rapamycin effects in the Tsc1+/- mouse model

BACKGROUND

Tuberous sclerosis complex (TSC) is a rare monogenic disorder characterized by benign tumors in multiple organs as well as a high prevalence of epilepsy, intellectual disability and autism. TSC is caused by inactivating mutations in the TSC1 or TSC2 genes. Heterozygocity induces hyperactivation of mTOR which can be inhibited by mTOR inhibitors, such as rapamycin, which have proven efficacy in the treatment of TSC-associated symptoms. The aim of the present study was (1) to identify molecular changes associated with social and cognitive deficits in the brain tissue of Tsc1+/- mice and (2) to investigate the molecular effects of rapamycin treatment, which has been shown to ameliorate genotype-related behavioural deficits.

METHODS

Molecular alterations in the frontal cortex and hippocampus of Tsc1+/- and control mice, with or without rapamycin treatment, were investigated. A quantitative mass spectrometry-based shotgun proteomic approach (LC-MSE) was employed as an unbiased method to detect changes in protein levels. Changes identified in the initial profiling stage were validated using selected reaction monitoring (SRM). Protein Set Enrichment Analysis was employed to identify dysregulated pathways.

RESULTS

LC-MSE analysis of Tsc1+/- mice and controls (n = 30) identified 51 proteins changed in frontal cortex and 108 in the hippocampus. Bioinformatic analysis combined with targeted proteomic validation revealed several dysregulated molecular pathways. Using targeted assays, proteomic alterations in the hippocampus validated the pathways "myelination", "dendrite," and "oxidative stress", an upregulation of ribosomal proteins and the mTOR kinase. LC-MSE analysis was also employed on Tsc1+/- and wildtype mice (n = 34) treated with rapamycin or vehicle. Rapamycin treatment exerted a stronger proteomic effect in Tsc1+/- mice with significant changes (mainly decreased expression) in 231 and 106 proteins, respectively. The cellular pathways "oxidative stress" and "apoptosis" were found to be affected in Tsc1+/- mice and the cellular compartments "myelin sheet" and "neurofilaments" were affected by rapamycin treatment. Thirty-three proteins which were altered in Tsc1+/- mice were normalized following rapamycin treatment, amongst them oxidative stress related proteins, myelin-specific and ribosomal proteins.

CONCLUSIONS

Molecular changes in the Tsc1+/- mouse brain were more prominent in the hippocampus compared to the frontal cortex. Pathways linked to myelination and oxidative stress response were prominently affected and, at least in part, normalized following rapamycin treatment. The results could aid in the identification of novel drug targets for the treatment of cognitive, social and psychiatric symptoms in autism spectrum disorders. Similar pathways have also been implicated in other psychiatric and neurodegenerative disorders and could imply similar disease processes. Thus, the potential efficacy of mTOR inhibitors warrants further investigation not only for autism spectrum disorders but also for other neuropsychiatric and neurodegenerative diseases.

A mass spectrometry-based proteomic analysis of Homer2-interacting proteins in the mouse brain

In the brain, the Homer protein family modulates excitatory signal transduction and receptor plasticity through interactions with other proteins in dendritic spines. Homer proteins are implicated in a variety of psychiatric disorders such as schizophrenia and addiction. Since long Homers serve as scaffolding proteins, identifying their interacting partners is an important first step in understanding their biological function and could help to guide the design of new therapeutic strategies. The present study set out to document Homer2-interacting proteins in the mouse brain using a co-immunoprecipitation-based mass spectrometry approach where Homer2 knockout samples were used to filter out non-specific interactors. We found that in the mouse brain, Homer2 interacts with a limited subset of its previously reported interacting partners (3 out of 31). Importantly, we detected an additional 15 novel Homer2-interacting proteins, most of which are part of the N-methyl-D-aspartate receptor signaling pathway. These results corroborate the central role Homer2 plays in glutamatergic transmission and expand the network of proteins potentially contributing to the behavioral abnormalities associated with altered Homer2 expression.

SIGNIFICANCE

Long Homer proteins are scaffolding proteins that regulate signal transduction in neurons. Identifying their interacting partners is key to understanding their function. We used co-immunoprecipitation in combination with mass spectrometry to establish the first comprehensive list of Homer2-interacting partners in the mouse brain. The specificity of interactions was evaluated using Homer2 knockout brain tissue as a negative control. The set of proteins that we identified minimally overlaps with previously reported interacting partners of Homer2; however, we identified novel interactors that are part of a signaling cascade activated by glutamatergic transmission, which improves our mechanistic understanding of the role of Homer2 in behavior.

Proteomics investigation of OSCC-specific salivary biomarkers in a Hungarian population highlights the importance of identification of population-tailored biomarkers

Oral squamous cell carcinoma (OSCC) accounting for about 90% of malignant oral lesions is the 6th most common malignancy worldwide. Diagnostic delay may contribute to dismal survival rate therefore, there is a need for developing specific and sensitive biomarkers to improve early detection. Hungarian population occupies the top places of statistics regarding OSCC incidence and mortality figures therefore, we aimed at finding potential salivary protein biomarkers suitable for the Hungarian population. In this study we investigated 14 proteins which were previously reported as significantly elevated in saliva of patients with OSCC. In case of IL-1α, IL-1β, IL-6, IL-8, TNF-α and VEGF a Luminex-based multiplex kit was utilized and the salivary concentrations were determined. In case of catalase, profilin-1, S100A9, CD59, galectin-3-bindig protein, CD44, thioredoxin and keratin-19, SRM-based targeted proteomic method was developed and the relative amount of the proteins was determined in the saliva of patients with OSCC and controls. After several rounds of optimization and using stable isotope-containing peptides, we developed an SRM-based method for rapid salivary protein detection. The validation of the selected potential biomarkers by ELISA revealed salivary protein S100A9 and IL-6 as useful protein biomarkers for OSCC detection improving the diagnostic accuracy for OSCC in the Hungarian population.A noninvasive diagnostic method to detect biomarkers useful for the early diagnosis of OSCC was developed. This can be an attractive strategy in screening saliva samples collected in a nation-wide multi-centric study in order to decrease morbidity, mortality, to enhance survival rate and to improve quality of life. The heterogeneity of protein biomarkers found in different ethnic groups presented in the literature highlights the importance of identification of population-tailored protein biomarkers.