Screening and Validation of Novel Biomarkers in Osteoarticular Pathologies by Comprehensive Combination of Protein Array Technologies

Enhancement of Tumor Cell Immunogenicity and Antitumor Properties Derived from Platinum-Conjugated Iron Nanoparticles

From chemistry design to clinical application, several approaches have been developed to overcome platinum drawbacks in antitumoral therapies. An in-depth understanding of intracellular signaling may hold the key to the relationship of both conventional drugs and nanoparticles. Within these strategies, first, nanotechnology has become an essential tool in oncotherapy, improving biopharmaceutical properties and providing new immunomodulatory profiles to conventional drugs mediated by activation of endoplasmic reticulum (ER) stress. Secondly, functional proteomics techniques based on microarrays have proven to be a successful method for high throughput screening of proteins and profiling of biomolecule mechanisms of action. Here, we conducted a systematic characterization of the antitumor profile of a platinum compound conjugated with iron oxide nanoparticles (IONPs). As a result of the nano-conjugation, cytotoxic and proteomics profiles revealed a significant improvement in the antitumor properties of the starting material, providing selectivity in certain tumor cell lines tested. Moreover, cell death patterns associated with immunogenic cell death (ICD) response have also been identified when ER signaling pathways have been triggered. The evaluation in several tumor cell lines and the analysis by functional proteomics techniques have shown novel perspectives on the design of new cisplatin-derived conjugates, the high value of IONPs as drug delivery systems and ICD as a rewarding approach for targeted oncotherapy and onco-immunotherapies.

Exploring High-Throughput Immunoassays for Biomarker Validation in Rheumatic Diseases in the Context of the Human Proteome Project

Rheumatic diseases are high prevalence pathologies with different etiology and evolution and low sensitivity in clinical diagnosis. Therefore, it is necessary to develop an early diagnosis method which allows personalized treatment, depending on the specific pathology. The biology/disease initiative, at Human Proteome Project, is an integrative approach to identify relevant proteins in the human proteome associated with pathologies. A previously reported literature data mining analysis, which identified proteins related to osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PSA) was used to establish a systematic prioritization of potential biomarkers candidates for further evaluation by functional proteomics studies. The aim was to study the protein profile of serum samples from patients with rheumatic diseases such as OA, RA, and PSA. To achieve this goal, customized antibody microarrays (containing 151 antibodies targeting 121 specific proteins) were used to identify biomarkers related to early and specific diagnosis in a screening of 960 serum samples (nondepleted) (OA, n = 480; RA, n = 192; PSA, n = 288). This functional proteomics screening has allowed the determination of a panel (30 serum proteins) as potential biomarkers for these rheumatic diseases, displaying receiver operating characteristics curves with area under the curve values of 80-90%.

Systematic evaluation of plasma signaling cascades by functional proteomics approaches: SARS-CoV-2 infection as model

PURPOSE

Acute phase reactants (APRs) play a critical role in inflammation. The difference in their physiological functions or the different dynamic ranges of these proteins in plasma makes it difficult to detect them simultaneously and to use several of these proteins as a tool in clinical practice.

EXPERIMENTAL DESIGN

A novel multiplex assay has been designed and optimized to carry out a high-throughput and simultaneous screening of APRs, allowing the detection of each of them at the same time and in their corresponding dynamic range.

RESULTS

Using Sars-CoV-2 infection as a model, it has been possible to profile different patterns of acute phase proteins that vary significantly between healthy and infected patients. In addition, severity profiles (acute respiratory distress syndrome and sepsis) have been established.

CONCLUSIONS AND CLINICAL RELEVANCE

Differential profiles in acute phase proteins can serve as a diagnostic and prognostic tool, among patient stratification. The design of this new platform for their simultaneous detection paves the way for them to be more extensive use in clinical practice.

Mass spectrometry-based proteomics identify novel serum osteoarthritis biomarkers

Background

Osteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.

Methods

Serum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography–tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.

Results

In total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).

Conclusion

Our study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02801-1.

SARS-CoV-2 Infection Triggers Auto-Immune Response in ARDS

Acute respiratory distress syndrome (ARDS) is a severe pulmonary disease, which is one of the major complications in COVID-19 patients. Dysregulation of the immune system and imbalances in cytokine release and immune cell activation are involved in SARS-CoV-2 infection. Here, the inflammatory, antigen, and auto-immune profile of patients presenting COVID-19-associated severe ARDS has been analyzed using functional proteomics approaches. Both, innate and humoral responses have been characterized through acute-phase protein network and auto-antibody signature. Severity and sepsis by SARS-CoV-2 emerged to be correlated with auto-immune profiles of patients and define their clinical progression, which could provide novel perspectives in therapeutics development and biomarkers of COVID-19 patients. Humoral response in COVID-19 patients’ profile separates with significant differences patients with or without ARDS. Furthermore, we found that this profile can be correlated with COVID-19 severity and results more common in elderly patients.

Deciphering Biomarkers for Leptomeningeal Metastasis in Malignant Hemopathies (Lymphoma/Leukemia) Patients by Comprehensive Multipronged Proteomics Characterization of Cerebrospinal Fluid

Simple Summary

The early diagnosis of leptomeningeal disease is a challenge because it is asymptomatic in the early stages. Consequently, it is important to identify a panel of biomarkers to help in its diagnosis and/or prognosis. For this purpose, we explored a multipronged proteomics approach in cerebrospinal fluid (CSF) to determine a potential panel of biomarkers. Thus, a systematic and exhaustive characterization of more than 300 CSF samples was performed by an integrated approach by Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) and functional proteomics analysis to establish protein profiles, which were useful for developing a panel of biomarkers validated by in silico approaches.

Abstract

In the present work, leptomeningeal disease, a very destructive form of systemic cancer, was characterized from several proteomics points of view. This pathology involves the invasion of the leptomeninges by malignant tumor cells. The tumor spreads to the central nervous system through the cerebrospinal fluid (CSF) and has a very grim prognosis; the average life expectancy of patients who suffer it does not exceed 3 months. The early diagnosis of leptomeningeal disease is a challenge because, in most of the cases, it is an asymptomatic pathology. When the symptoms are clear, the disease is already in the very advanced stages and life expectancy is low. Consequently, there is a pressing need to determine useful CSF proteins to help in the diagnosis and/or prognosis of this disease. For this purpose, a systematic and exhaustive proteomics characterization of CSF by multipronged proteomics approaches was performed to determine different protein profiles as potential biomarkers. Proteins such as PTPRC, SERPINC1, sCD44, sCD14, ANPEP, SPP1, FCGR1A, C9, sCD19, and sCD34, among others, and their functional analysis, reveals that most of them are linked to the pathology and are not detected on normal CSF. Finally, a panel of biomarkers was verified by a prediction model for leptomeningeal disease, showing new insights into the research for potential biomarkers that are easy to translate into the clinic for the diagnosis of this devastating disease.

Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model

Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.

Multiomics Profiling of Alzheimer's Disease Serum for the Identification of Autoantibody Biomarkers

New biomarkers of Alzheimer's disease (AD) with a diagnostic value in preclinical and prodromal stages are urgently needed. AD-related serum autoantibodies are potential candidate biomarkers. Here, we aimed at identifying AD-related serum autoantibodies using protein microarrays and mass spectrometry-based methods. To this end, an untargeted complementary screening using high-density (42,100 antigens) and low-density (384 antigens) planar protein-epitope signature tag (PrEST) arrays and an immunoprecipitation protocol coupled to mass spectrometry analysis were used for serum autoantibody profiling. From the untargeted screening phase, 377 antigens corresponding to 338 proteins were selected for validation. Out of them, IVD, CYFIP1, and ADD2 seroreactivity was validated using 128 sera from AD patients and controls by PrEST-suspension bead arrays, and ELISA or luminescence Halotag-based bead immunoassay using full-length recombinant proteins. Importantly, IVD, CYFIP1, and ADD2 showed in combination a noticeable AD diagnostic ability. Moreover, IVD protein abundance in the prefrontal cortex was significantly two-fold higher in AD patients than in controls by western blot and immunohistochemistry, whereas CYFIP1 and ADD2 were significantly down-regulated in AD patients. The panel of AD-related autoantigens identified by a comprehensive multiomics approach may provide new insights of the disease and should help in the blood-based diagnosis of Alzheimer's disease. Mass spectrometry raw data are available in the ProteomeXchange database with the access number PXD028392.

Cholesterol metabolism related genes in osteoarthritis

Cholesterol homeostasis plays a significant role in skeletal development and the dysregulation of cholesterol-related mechanism has been shown to be involved in the development of cartilage diseases including osteoarthritis (OA). Epidemiological studies have shown an association between elevated serum cholesterol levels and OA. Furthermore, abnormal lipid accumulation in chondrocytes as a result of abnormal regulation of cholesterol homeostasis has been demonstrated to be involved in the development of OA. Although, many in vivo and in vitro studies support the connection between cholesterol and cartilage degradation, the mechanisms underlying the complex interactions between lipid metabolism, especially HDL cholesterol metabolism, and OA remain unclear. The current review aims to address this problem and focuses on key molecular players of the HDL metabolism pathway and their role in ΟΑ pathogenesis. Understanding the complexity of biological processes implicated in OA pathogenesis, such as cholesterol metabolism, may lead to new targets for drug therapy of OA patients.

Association of acute psychosocial stress with oxidative stress: Evidence from serum analysis

Growing evidence implicates an association between psychosocial stress and oxidative stress (OxSt) although there are not yet reliable biomarkers to study this association. We used a Trier Social Stress Test (TSST) and compared the response of a healthy control group (HC; N=10) against the response of a schizophrenia group (SCZ; N=10) that is expected to have higher levels of OxSt. Because our previous study showed inconsistent changes in conventional molecular markers for stress responses in the neuroendocrine and immune systems, we analyzed the same serum samples using a separate reducing capacity assay that provides a more global measurement of OxSt. This assay uses the moderately strong oxidizing agent iridium (Ir) to probe a sample's reducing capacity. Specifically, we characterized OxSt by this Ir-reducing capacity assay (Ir-RCA) using two measurement modalities (optical and electrochemical) and we tuned this assay by imposing an input voltage sequence that generates multiple output metrics for data-driven analysis. We defined five OxSt metrics (one optical and four electrochemical metrics) and showed: (i) internal consistency among each metric in the measurements of all 40 samples (baseline and post TSST for N=20); (ii) all five metrics were consistent with expectations of higher levels of OxSt for the SCZ group (three individual metrics showed statistically significant differences); and (iii) all five metrics showed higher levels of OxSt Post-TSST (one metric showed statistically significant difference). Using multivariant analysis, we showed that combinations of OxSt metrics could discern statistically significant increases in OxSt for both the SCZ and HC groups 90 min after the imposed acute psychosocial stress.

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Ir-reducing capacity assay (Ir-RCA) provides a robust global measure of oxidative stress in serum.

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The multiple oxidative stress (OxSt) output metrics of this Ir-RCA are useful for data-driven analysis.

•

The combination of OxSt metrics can discern significant increases in OxStwithin 90 mins of an imposed psychosocial stress.

Systematic and Rational Design of Protein Arrays in Noncontact Printers: Pipeline and Critical Aspects

The systematic design and construction of customized protein microarrays are critical for the further successful screening of biological samples in biomedical research projects. In general protein microarrays are classified according to the content, detection method, and printing methodology, among others. Here, we are focused on the type of printing: contact and noncontact. Both approaches have advantages and disadvantages; however, in any of the approaches, a prior well design and systematic preparation of materials and/or instruments required for the customized antibody arrays is critical. In this chapter, the process for an antibody microarray by a noncontact printer is described in detail from the preparation of array content to the analysis, including quality control steps.

The Role of Concentration on Drop Formation and Breakup of Collagen, Fibrinogen, and Thrombin Solutions during Inkjet Bioprinting

The influence of protein concentration on drop formation and breakup of aqueous solutions of fibrous proteins collagen and fibrinogen and globular protein thrombin in different concentration regimes has been investigated during drop-on-demand (DOD) inkjet printing. The capillary-driven thinning and breakup of dilute collagen, fibrinogen, and thrombin solutions, the solutions in which protein molecules are far away from each other, are predominantly resisted by inertial force. Although the capillary-driven thinning and breakup of semidilute unentangled collagen and fibrinogen solutions, the solutions in which protein molecules begin to interpenetrate each other, are predominantly resisted by inertial force on the initial onset of necking, the breakup of droplets is delayed because of the resistance of elastic force. The resistance of viscous force to the necking and breakup of both the dilute and semidilute unentangled protein solutions is negligible. Aggregates or subvisible particles (between 1 and 100 μm) constantly disrupt the formation of droplets for the semidilute unentangled protein solutions, even when their inverse Ohnesorge number (Z) is within the printability range of 4 ≤ Z ≤ 14. Although aggregates are present in the dilute protein solutions, they do not disrupt the formation of droplets.

Rheological investigation of collagen, fibrinogen, and thrombin solutions for drop-on-demand 3D bioprinting

Collagen, fibrinogen, and thrombin proteins in aqueous buffer solutions are widely used as precursors of natural biopolymers in three-dimensional (3D) bioprinting applications. The proteins are sourced from animals and their quality may vary from batch to batch, inducing differences in the rheological properties of such solutions. In this work, we investigate the rheological response of collagen, fibrinogen, and thrombin protein solutions in bulk and at the solution/air interface. Interfacial rheological measurements show that fibrous collagen, fibrinogen and globular thrombin proteins adsorb and aggregate at the solution/air interface, forming a viscoelastic solid film at the interface. The viscoelastic film corrupts the bulk rheological measurements in rotational rheometers by contributing to an apparent yield stress, which increases the apparent bulk viscosity up to shear rates as high as 1000 s^-1. The addition of a non-ionic surfactant, such as polysorbate 80 (PS80) in small amounts between 0.001 and 0.1 v/v%, prevents the formation of the interfacial layer, allowing the estimation of true bulk viscosity of the solutions. The estimation of viscosity not only helps in identifying those protein solutions that are potentially printable with drop-on-demand (DOD) inkjet printing but also detects inconsistencies in flow behavior among the batches.

Protein profiling and network enrichment analysis in individuals before and after the onset of rheumatoid arthritis

Background

Antibodies and upregulated cytokines and chemokines predate the onset of rheumatoid arthritis (RA) symptoms. We aimed to identify the pathways related to the early processes leading to RA development, as well as potential novel biomarkers, using multiple protein analyses.

Methods

A case-control study was conducted within the Biobank of northern Sweden. The plasma samples from 118 pre-symptomatic individuals (207 samples; median predating time 4.1 years), 79 early RA patients, and 74 matched controls were analyzed. The levels of 122 unique proteins with an acknowledged relationship to autoimmunity were analyzed using 153 antibodies and a bead-based multiplex system (FlexMap3D; Luminex Corp.). The data were analyzed using multifactorial linear regression model, random forest, and network enrichment analysis (NEA) based on the 10 most significantly differentially expressed proteins for each two-by-two group comparison, using the MSigDB collection of hallmarks.

Results

There was a high agreement between the different statistical methods to identify the most significant proteins. The adipogenesis and interferon alpha response hallmarks differentiated pre-symptomatic individuals from controls. These two hallmarks included proteins involved in innate immunity. Between pre-symptomatic individuals and RA patients, three hallmarks were identified as follows: apical junction, epithelial mesenchymal transition, and TGF-β signaling, including proteins suggestive of cell interaction, remodulation, and fibrosis. The adipogenesis and heme metabolism hallmarks differentiated RA patients from controls.

Conclusions

We confirm the importance of interferon alpha signaling and lipids in the early phases of RA development. Network enrichment analysis provides a tool for a deeper understanding of molecules involved at different phases of the disease progression.

Oncoproteomics: Current status and future opportunities

Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.

A Systematic Analysis Workflow for High-Density Customized Protein Microarrays in Biomarker Screening

High-density protein microarrays constitute a promising high-throughput platform for the characterization of protein expression patterns, biomarker discovery, and validation. Different types of protein microarrays have been described according to several features (such as content, format, and detection system) presenting advantages and disadvantages which are relevant for the specific application and purposes. Therefore, an experimental design is key for any screening based on protein microarrays assays; in fact, the data analysis strategy is directly related to the experimental design, type of protein microarray and consequently the final outcome, the data and results interpretation, is also directly linked. Here, it is proposed a systematic workflow for biomarker discovery based on tailor-made protein microarrays platforms which obtain comprehensively info for the functional protein characterization in high-throughput format.

Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project

The Human Proteome Project (HPP) annually reports on progress throughout the field in credibly identifying and characterizing the human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2018-01-17, the baseline for this sixth annual HPP special issue of the Journal of Proteome Research, contains 17 470 PE1 proteins, 89% of all neXtProt predicted PE1-4 proteins, up from 17 008 in release 2017-01-23 and 13 975 in release 2012-02-24. Conversely, the number of neXtProt PE2,3,4 missing proteins has been reduced from 2949 to 2579 to 2186 over the past two years. Of the PE1 proteins, 16 092 are based on mass spectrometry results, and 1378 on other kinds of protein studies, notably protein-protein interaction findings. PeptideAtlas has 15 798 canonical proteins, up 625 over the past year, including 269 from SUMOylation studies. The largest reason for missing proteins is low abundance. Meanwhile, the Human Protein Atlas has released its Cell Atlas, Pathology Atlas, and updated Tissue Atlas, and is applying recommendations from the International Working Group on Antibody Validation. Finally, there is progress using the quantitative multiplex organ-specific popular proteins targeted proteomics approach in various disease categories.

Soluble biochemical markers of osteoarthritis: Are we close to using them in clinical practice?

Osteoarthritis (OA) is the most common form of arthritis and a major cause of pain and disability. Recent work suggests that the global burden of OA is increasing, and costs associated with treatment are expected to increase dramatically as the aging human population expands. OA is currently diagnosed using radiography, but this technique is an indirect and insensitive measure of alterations in articular cartilage and fails to measure dynamic inflammatory processes in the joint. Radiographic changes detected overtime are small and occur in only a subset (progressors) of patients with OA. Therefore, we diagnose patients with OA on the basis of a diagnostic classification that is outdated. We also use the same tools and approaches for assessing the efficacy of new pharmacological and nonpharmacological interventions. In this review, we discuss the utility of soluble biochemical markers as biomarkers of OA and discuss whether we are close to using them in clinical practice. Combining patient information, functional imaging and carefully selected panels of biomarkers can help in achieving enhanced patient stratification and lead to better designed clinical trials. Biomarkers can be used for molecular endotyping and for developing more effective and more personalized treatments that will enhance clinical care for patients with OA.

What did we learn from 'omics' studies in osteoarthritis

PURPOSE OF REVIEW

'Omics' technologies developed for the massive analysis of the major biologically relevant molecules (genes, proteins, metabolites) have been applied to the study of osteoarthritis (OA) for more than a decade.

RECENT FINDINGS

'Omics' studies have undoubtedly contributed to increase the knowledge on pathogenic processes related with OA and have provided hundreds to thousands of molecules that might have a putative biomarker utility for this disease.

SUMMARY

This review describes the most recent 'omics' studies in OA research, their conclusions, and discuss those remaining challenges. Still many validation studies must be performed in large and well-characterized cohorts for the translation of the findings from 'omics' strategies to clinical applications. The development of tools for the intelligent integration of 'omics' data with clinical and imaging information is also mandatory to take full profit of the work that has been already performed.