The use of proteomics to identify novel therapeutic targets for the treatment of disease

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Contribution of Capillary Zone Electrophoresis Hyphenated with Drift Tube Ion Mobility Mass Spectrometry as a Complementary Tool to Microfluidic Reversed Phase Liquid Chromatography for Antigen Discovery

The discovery of new antigens specific to multiple myeloma that could be targeted by novel immunotherapeutic approaches is currently of great interest. To this end, it is important to increase the number of proteins identified in the sample by combining different separation strategies. A capillary zone electrophoresis (CZE) method, coupled with drift tube ion mobility (DTIMS) and quadrupole time-of-flight mass spectrometry (QTOF), was developed for antigen discovery using the human myeloma cell line LP-1. This method was first optimized to obtain a maximum number of identifications. Then, its performance in terms of uniqueness of identifications was compared to data acquired by a microfluidic reverse phase liquid chromatography (RPLC) method. The orthogonality of these two approaches and the physicochemical properties of the entities identified by CZE and RPLC were evaluated. In addition, the contribution of DTIMS to CZE was investigated in terms of orthogonality as well as the ability to provide unique information. In conclusion, we believe that the combination of CZE-DTIMS-QTOF and microfluidic RPLC provides unique information in the context of antigen discovery.

Insights on Proteomics-Driven Body Fluid-Based Biomarkers of Cervical Cancer

Cervical cancer is one of the top malignancies in women around the globe, which still holds its place despite being preventable at early stages. Gynecological conditions, even maladies like cervical cancer, still experience scrutiny from society owing to prevalent taboo and invasive screening methods, especially in developing economies. Additionally, current diagnoses lack specificity and sensitivity, which prolong diagnosis until it is too late. Advances in omics-based technologies aid in discovering differential multi-omics profiles between healthy individuals and cancer patients, which could be utilized for the discovery of body fluid-based biomarkers. Body fluids are a promising potential alternative for early disease detection and counteracting the problems of invasiveness while also serving as a pool of potential biomarkers. In this review, we will provide details of the body fluids-based biomarkers that have been reported in cervical cancer. Here, we have presented our perspective on proteomics for global biomarker discovery by addressing several pertinent problems, including the challenges that are confronted in cervical cancer. Further, we also used bioinformatic methods to undertake a meta-analysis of significantly up-regulated biomolecular profiles in CVF from cervical cancer patients. Our analysis deciphered alterations in the biological pathways in CVF such as immune response, glycolytic processes, regulation of cell death, regulation of structural size, protein polymerization disease, and other pathways that can cumulatively contribute to cervical cancer malignancy. We believe, more extensive research on such biomarkers, will speed up the road to early identification and prevention of cervical cancer in the near future.

GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning

The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target prioritization demonstrated a clear superiority compared to classical feature engineering approaches. Our work highlights the potential of attributed network representation learning for target prioritization. Given the overarching relevance of networks in computational biology we believe that attributed network representation learning techniques could have a broader impact in the future.

Using Antibody Arrays for Biomarker Discovery

Biomarkers for diseases are important for the development of clinical diagnostic tests and can provide early intervention for cancer or cardiovascular patients. Over the past decade, antibody array technology has achieved significant technological improvement in the quantitative measurement of more than a thousand proteins simultaneously and has been utilized to screen and identify unique proteins as disease biomarkers. However, few biomarkers have been translated into clinical application. This chapter will discuss the protocol for the screening and validation of unique proteins that create a new avenue for biomarker discovery.

Proteomics and functional study reveal marginal zone B and B1 cell specific protein as a candidate marker of multiple myeloma

Multiple myeloma (MM) is a plasma cell‑associated cancer and accounts for 13% of all hematological malignancies, worldwide. MM still remains an incurable plasma cell malignancy with a poor prognosis due to a lack of suitable markers. Therefore, discovering novel markers and targets for diagnosis and therapeutics of MM is essential. The present study aims to identify markers associated with MM malignancy using patient‑derived MM mononuclear cells (MNCs). Label‑free quantitative proteomics analysis revealed a total of 192 differentially regulated proteins, in which 79 proteins were upregulated and 113 proteins were found to be downregulated in MM MNCs as compared to non‑hematological malignant samples. The identified differentially expressed candidate proteins were analyzed using various bioinformatics tools, including Ingenuity Pathway Analysis (IPA), Protein Analysis THrough Evolutionary Relationships (PANTHER), Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Database for Annotation, Visualization and Integrated Discovery (DAVID) to determine their biological context. Among the 192 candidate proteins, marginal zone B and B1 cell specific protein (MZB1) was investigated in detail using the RPMI-8226 cell line model of MM. The functional studies revealed that higher expression of MZB1 is associated with promoting the progression of MM pathogenesis and could be established as a potential target for MM in the future.

Iran's Contribution to Human Proteomic Research

Proteomics is a powerful approach to study the whole set of proteins expressed in an organism, organ, tissue or cell resulting in valuable information on physiological or pathological state of a biological system. High throughput proteomic data facilitated the understanding of various biological systems with respect to normal and pathological conditions particularly in the instances of human clinical manifestations. The important role of proteins as the functional gene products encouraged scientists to apply this technology to gain a better understanding of extremely complex biological systems. In last two decades, several proteomics teams have been gradually formed in Iran. In this review, we highlight the most important findings of proteomic research groups in Iran at various areas of stem cells, Y chromosome, infertility, infectious disease and biomarker discovery.

LC-MS analysis of Hep-2 and Hek-293 cell lines treated with Brazilian red propolis reveals differences in protein expression

Objective

Red propolis, an exclusive variety of propolis found in the northeast of Brazil has shown to present antitumour activity, among several other biological properties. This article aimed to help to evaluate the underlying molecular mechanisms of the potential anticancer effects of red propolis on tumour, Hep-2, and non-tumour cells, Hek-293.

Methods

Differentially expressed proteins in human cell lines were identified through label-free quantitative MS-based proteomic platform, and cells were stained with Giemsa to show morphological changes.

Key findings

A total of 1336 and 773 proteins were identified for Hep-2 and Hek-293, respectively. Among the proteins here identified, 16 were regulated in the Hep-2 cell line and 04 proteins in the Hek-293 line. Over a total of 2000 proteins were identified under MS analysis, and approximately 1% presented differential expression patterns. The GO annotation using Protein Analysis THrough Evolutionary Relationships classification system revealed predominant molecular function of catalytic activity, and among the biological processes, the most prominent was associated to cell metabolism.

Conclusion

The proteomic profile here presented should help to elucidate further molecular mechanisms involved in inhibition of cancer cell proliferation by red propolis, which remain unclear to date.

Prediction of drug response and safety in clinical practice

Many clinicians hoped that the completion of the Human Genome Project would result in "individualized drug therapy," i.e., determining the right medication at the right dose 100% of the time based upon the individual's genetics. The pharmacogenomic prediction of drug efficacy and safety has not become a reality due to continuing realization of the complexity dictating the human-drug interaction. New methods of metabolomics, proteomics, and transcriptomics that account for this complexity hold promise for translational researchers hoping to increase drug efficacy and decrease drug toxicity.

Emerging nanoproteomics approaches for disease biomarker detection: a current perspective

Availability of genome sequence of human and different pathogens has advanced proteomics research for various clinical applications. One of the prime goals of proteomics is identification and characterization of biomarkers for cancer and other fatal human diseases to aid an early diagnosis and monitor disease progression. However, rapid detection of low abundance biomarkers from the complex biological samples under clinically relevant conditions is extremely difficult, and it requires the development of ultrasensitive, robust and high-throughput technological platform. In order to overcome several technical limitations associated with sensitivity, dynamic range, detection time and multiplexing, proteomics has started integrating several emerging disciplines such as nanotechnology, which has led to the development of a novel analytical platform known as 'nanoproteomics'. Among the diverse classes of nanomaterials, the quantum dots, gold nanoparticles, carbon nanotubes and silicon nanowires are the most promising candidates for diagnostic applications. Nanoproteomics offers several advantages such as ultralow detection, short assay time, high-throughput capability and low sample consumption. In this article, we have discussed the application of nanoproteomics for biomarker discovery in various diseases with special emphasis on various types of cancer. Furthermore, we have discussed the prospects, merits and limitations of nanoproteomics.

Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead

Serum is an ideal biological sample that contains an archive of information due to the presence of a variety of proteins released by diseased tissue, and serum proteomics has gained considerable interest for the disease biomarker discovery. Easy accessibility and rapid protein changes in response to disease pathogenesis makes serum an attractive sample for clinical research. Despite these advantages, the analysis of serum proteome is very challenging due to the wide dynamic range of proteins, difficulty in finding low-abundance target analytes due to the presence of high-abundance serum proteins, high levels of salts and other interfering compounds, variations among individuals and paucity of reproducibility. Sample preparation introduces pre-analytical variations and poses major challenges to analyze the serum proteome. The label-free detection techniques such as surface plasmon resonance, microcantilever, few nanotechniques and different resonators are rapidly emerging for the analysis of serum proteome and they have exhibited potential to overcome few limitations of the conventional techniques. In this article, we will discuss the current status of serum proteome analysis for the biomarker discovery and address key technological advancements, with a focus on challenges and amenable solutions.

Proteomic analysis and identification of ladasten target proteins in rat brain

Two-dimensional electrophoresis and mass spectrometry detection were used to evaluate the range of proteins in rat brain after single treatment with ladasten (50 mg/kg). We identified 13 proteins with various levels of expression.

Biomarkers and vasospasm after aneurysmal subarachnoid hemorrhage

Subarachnoid hemorrhage from the rupture of a saccular aneurysm is a devastating neurological disease that has a high morbidity and mortality not only from the initial hemorrhage, but also from the delayed complications, such as cerebral vasospasm. Cerebral vasospasm can lead to delayed ischemic injury 1 to 2 weeks after the initial hemorrhage. Although the pathophysiology of vasospasm has been described for decades, the molecular basis remains poorly understood. With the many advances in the past decade in the development of sensitive molecular biological techniques, imaging, biochemical purification, and protein identification, new insights are beginning to reveal the etiology of vasospasm. These findings will not only help to identify markers of vasospasm and prognostic outcome, but will also yield potential therapeutic targets for the treatment of this disease. This review focuses on the methods available for the identification of biological markers of vasospasm and their limitations, the current understanding as to the utility and prognostic significance of identified biomarkers, the utility of these biomarkers in predicting vasospasm and outcome, and future directions of research in this field.

A pathway-based view of human diseases and disease relationships

It is increasingly evident that human diseases are not isolated from each other. Understanding how different diseases are related to each other based on the underlying biology could provide new insights into disease etiology, classification, and shared biological mechanisms. We have taken a computational approach to studying disease relationships through 1) systematic identification of disease associated genes by literature mining, 2) associating diseases to biological pathways where disease genes are enriched, and 3) linking diseases together based on shared pathways. We identified 4,195 candidate disease associated genes for 1028 diseases. On average, about 50% of disease associated genes of a disease are statistically mapped to pathways. We generated a disease network which consists of 591 diseases and 6,931 disease relationships. We examined properties of this network and provided examples of novel disease relationships which cannot be readily captured through simple literature search or gene overlap analysis. Our results could potentially provide insights into the design of novel, pathway-guided therapeutic interventions for diseases.

Proteomic analysis of rat brains following exposure to electroconvulsive therapy

Electroconvulsive therapy (ECT) is one of the most effective treatments used in psychiatry to date. The mechanisms of ECT action, however, are the least understood and still unclear. As a tool to elucidate the mechanisms of action of ECT, we employed proteomic analysis based on the identification of differentially expressed proteins after exposure to repeated ECT in rat brains. The expression of proteins was visualized by silver stain after two-dimensional gel electrophoresis. Of 24 differentially expressed protein spots (p<0.05 by Student t-test), six different proteins from 7 spots were identified by matrix-assisted laser desorption/ionization time-of flight (MALDI-TOF)/mass spectrometry. Among the identified proteins, there were five dominantly expressed proteins in the ECT-treated rat brain tissues (p<0.05); S100 protein beta chain, 14-3-3 protein zeta/delta, similar to ubiquitin-like 1 (sentrin) activating enzyme subunit 1, suppressor of G2 allele of SKP1 homolog, and phosphatidylinositol transfer protein alpha. The expression of only one protein, ACY1 protein, was repressed (p<0.05). These findings likely serve for a better understanding of mechanisms involved in the therapeutic effects of ECT.

A simple, rapid, and convenient Luminex-compatible method of tissue isolation

Various pathological conditions are associated with changes in multiple protein biomarkers, and these changes can be assessed using xMAP beads and the Luminex platform. Although this platform is most commonly utilized in the analysis of biomarkers in serum, plasma, or urine, it is often desirable to measure these analytes in tissues (e.g., biopsy specimens). We have developed a simple, rapid method of tissue isolation in which excised tissues are permeabilized in RNAlater at 4 degrees C overnight, followed by Luminex analysis for inflammatory biomarkers (cytokines). This method was compared with flash-freezing in both mouse liver and human debrided wound specimens, and may be of utility in other clinical settings.

Proteomics of the human placenta: promises and realities

Proteomics is an area of study that sets as its ultimate goal the global analysis of all of the proteins expressed in a biological system of interest. However, technical limitations currently hamper proteome-wide analyses of complex systems. In a more practical sense, a desired outcome of proteomics research is the translation of large protein data sets into formats that provide meaningful information regarding clinical conditions (e.g., biomarkers to serve as diagnostic and/or prognostic indicators of disease). Herein, we discuss placental proteomics by describing existing studies, pointing out their strengths and weaknesses. In so doing, we strive to inform investigators interested in this area of research about the current gap between hyperbolic promises and realities. Additionally, we discuss the utility of proteomics in discovery-based research, particularly as regards the capacity to unearth novel insights into placental biology. Importantly, when considering under studied systems such as the human placenta and diseases associated with abnormalities in placental function, proteomics can serve as a robust 'shortcut' to obtaining information unlikely to be garnered using traditional approaches.