A highly efficient protein corona-based proteomic analysis strategy for the discovery of pharmacodynamic biomarkers

Design and Application of pH-Responsive Liposomes for Site-Specific Delivery of Cytotoxin from Cobra Venom

Background

Current immunotherapies with unexpected severe side effects and treatment resistance have not resulted in the desired outcomes for patients with melanoma, and there is a need to discover more effective medications. Cytotoxin (CTX) from Cobra Venom has been established to have favorable cytolytic activity and antitumor efficacy and is regarded as a promising novel anticancer agent. However, amphiphilic CTX with excellent anionic phosphatidylserine lipid-binding ability may also damage normal cells.

Methods

We developed pH-responsive liposomes with a high CTX load (CTX@PSL) for targeted acidic-stimuli release of drugs in the tumor microenvironment. The morphology, size, zeta potential, drug-release kinetics, and preservation stability were characterized. Cell uptake, apoptosis-promoting effects, and cytotoxicity were assessed using MTT assay and flow cytometry. Finally, the tissue distribution and antitumor effects of CTX@PSL were systematically assessed using an in vivo imaging system.

Results

CTX@PSL exhibited high drug entrapment efficiency, drug loading, stability, and a rapid release profile under acidic conditions. These nanoparticles, irregularly spherical in shape and small in size, can effectively accumulate at tumor sites (six times higher than free CTX) and are rapidly internalized into cancer cells (2.5-fold higher cell uptake efficiency). CTX@PSL displayed significantly stronger cytotoxicity (IC50 0.25 μg/mL) and increased apoptosis in than the other formulations (apoptosis rate 71.78±1.70%). CTX@PSL showed considerably better tumor inhibition efficacy than free CTX or conventional liposomes (tumor inhibition rate 79.78±5.93%).

Conclusion

Our results suggest that CTX@PSL improves tumor-site accumulation and intracellular uptake for sustained and targeted CTX release. By combining the advantages of CTX and stimuli-responsive nanotechnology, the novel CTX@PSL nanoformulation is a promising therapeutic candidate for cancer treatment.

Trypsin Encapsulation in the Zeolitic Imidazolate Framework for Low-Molecular Weight Protein Analysis with High Selectivity and Efficiency

Low-molecular weight proteins (LWPs) are important sources of biological information in biomarkers, signaling molecules, and pathology. However, the separation and analysis of LWPs in complex biological samples are challenging, mainly due to their low abundance and the complex sample pretreatment procedure. Herein, trypsin modified by poly(acrylic acid) (PAA) was encapsulated by a zeolitic imidazolate framework (ZIF-L). Mesopores were formed on the ZIF-L with the introduction of PAA. An alternative strategy for separation and pretreatment of LWPs was developed based on the prepared ZIF-L-encapsulated trypsin with adjustable pore size. The mesoporous structure of the prepared materials selectively excluded high-molecular weight proteins from the reaction system, allowing LWPs to enter the pores and react with the internal trypsin, resulting in an improved separation efficiency. The hydrophobicity of the ZIF-L simplified the digestion process by inducing significant structural changes in substrate proteins. In addition, the enzymatic activity was significantly enhanced by the developed encapsulation method that maintained the enzyme conformation, allowed low mass transfer resistance, and possessed a high enzyme-to-substrate ratio. As a result, the ZIF-L-encapsulated trypsin can achieve highly selective separation, valid denaturation, and efficient digestion of LWPs in a short time by simply mixing with substrate proteins, greatly simplifying the separation and pretreatment process of the traditional hydrolysis method. The prepared materials and the developed strategy demonstrated an excellent size-selective assay performance in model protein mixtures, showing great potential in the application of proteomics analysis.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

Insights into the time-course cellular effects triggered by iron oxide nanoparticles by combining proteomics with the traditional pharmacology strategy

In recent years, a number of initially approved magnetic iron oxide nanoparticle (IONP)-based nano-medicines have been withdrawn due to the obscure nano-bio effects. Therefore, there is an urgent need to study the cellular effects triggered by IONPs on cells. In this study, we investigate the time-course cellular effects on the response of RAW 264.7 cells caused by Si-IONPs via pharmacological and mass spectrometry-based proteomics techniques. Our results revealed that Si-IONPs were internalized by clathrin-mediated endocytosis within 1 hour, and gradually degraded in endolysosomes over time, which might influence autophagy, oxidative stress, innate immune response, and inflammatory response after 12 hours. Our research provides a necessary assessment of Si-IONPs for further clinical treatment.

Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins

Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.

MS Identification of Blood Plasma Proteins Concentrated on a Photocrosslinker-Modified Surface

This work demonstrates the use of a modified mica to concentrate proteins, which is required for proteomic profiling of blood plasma by mass spectrometry (MS). The surface of mica substrates, which are routinely used in atomic force microscopy (AFM), was modified with a photocrosslinker to allow "irreversible" binding of proteins via covalent bond formation. This modified substrate was called the AFM chip. This study aimed to determine the role of the surface and crosslinker in the efficient concentration of various types of proteins in plasma over a wide concentration range. The substrate surface was modified with a 4-benzoylbenzoic acid N-succinimidyl ester (SuccBB) photocrosslinker, activated by UV irradiation. AFM chips were incubated with plasma samples from a healthy volunteer at various dilution ratios (102X, 104X, and 106X). Control experiments were performed without UV irradiation to evaluate the contribution of physical protein adsorption to the concentration efficiency. AFM imaging confirmed the presence of protein layers on the chip surface after incubation with the samples. MS analysis of different samples indicated that the proteomic profile of the AFM-visualized layers contained common and unique proteins. In the working series of experiments, 228 proteins were identified on the chip surface for all samples, and 21 proteins were not identified in the control series. In the control series, a total of 220 proteins were identified on the chip surface, seven of which were not found in the working series. In plasma samples at various dilution ratios, a total of 146 proteins were identified without the concentration step, while 17 proteins were not detected in the series using AFM chips. The introduction of a concentration step using AFM chips allowed us to identify more proteins than in plasma samples without this step. We found that AFM chips with a modified surface facilitate the efficient concentration of proteins owing to the adsorption factor and the formation of covalent bonds between the proteins and the chip surface. The results of our study can be applied in the development of highly sensitive analytical systems for determining the complete composition of the plasma proteome.

Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.

Real-Time Optical Tracking of Protein Corona Formation on Single Nanoparticles in Serum

The formation of a protein corona, where proteins spontaneously adhere to the surface of nanomaterials in biological environments, leads to changes in their physicochemical properties and subsequently affects their intended biomedical functionalities. Most current methods to study protein corona formation are ensemble-averaging and either require fluorescent labeling, washing steps, or are only applicable to specific types of particles. Here we introduce real-time all-optical nanoparticle analysis by scattering microscopy (RONAS) to track the formation of protein corona in full serum, at the single-particle level, without any labeling. RONAS uses optical scattering microscopy and enables real-time and in situ tracking of protein adsorption on metallic and dielectric nanoparticles with different geometries directly in blood serum. We analyzed the adsorbed protein mass, the affinity, and the kinetics of the protein adsorption at the single particle level. While there is a high degree of heterogeneity from particle to particle, the predominant factor in protein adsorption is surface chemistry rather than the underlying nanoparticle material or size. RONAS offers an in-depth understanding of the mechanisms related to protein coronas and, thus, enables the development of strategies to engineer efficient bionanomaterials.

Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies

Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.

Ginsenoside Rk3 is a novel PI3K/AKT-targeting therapeutics agent that regulates autophagy and apoptosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. Ginsenoside Rk3, an important and rare saponin in heat-treated ginseng, is generated from Rg1 and has a smaller molecular weight. However, the anti-HCC efficacy and mechanisms of ginsenoside Rk3 have not yet been characterized. Here, we investigated the mechanism by which ginsenoside Rk3, a tetracyclic triterpenoid rare ginsenoside, inhibits the growth of HCC. We first explored the possible potential targets of Rk3 through network pharmacology. Both in vitro (HepG2 and HCC-LM3 cells) and in vivo (primary liver cancer mice and HCC-LM3 subcutaneous tumor-bearing mice) studies revealed that Rk3 significantly inhibits the proliferation of HCC. Meanwhile, Rk3 blocked the cell cycle in HCC at the G1 phase and induced autophagy and apoptosis in HCC. Further proteomics and siRNA experiments showed that Rk3 regulates the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway to inhibit HCC growth, which was validated by molecular docking and surface plasmon resonance. In conclusion, we report the discovery that ginsenoside Rk3 binds to PI3K/AKT and promotes autophagy and apoptosis in HCC. Our data strongly support the translation of ginsenoside Rk3 into novel PI3K/AKT-targeting therapeutics for HCC treatment with low toxic side effects.

Comprehensive and deep profiling of the plasma proteome with protein corona on zeolite NaY

Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the "masking" effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications.

Disease biomarker identification based on sample network optimization

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.