Proteome microarray technology and application: higher, wider, and deeper

Exploiting omic-based approaches to decipher Traditional Chinese Medicine

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese Medicine (TCM), an ancient health system, faces significant research challenges due to the complexity of its active components and targets, as well as a historical lack of detailed annotation. However, recent advances in omics technologies have begun to unravel these complexities, providing a more informed and nuanced understanding of TCM's therapeutic potential in contemporary healthcare.

AIM OF THE REVIEW

This review summarizes the application of omics technologies in TCM modernization, emphasizing components analysis, quality control, biomarker discovery, target identification, and treatment optimization. In addition, future perspectives on using omics for precision TCM treatment are also discussed.

MATERIALS AND METHODS

We have explored several databases (including PubMed, ClinicalTrials, Google Scholar, and Web of Science) to review related articles, focusing on Traditional Chinese Medicine, Omics Strategy, Precision Medicine, Biomarkers, Quality Control, and Molecular Mechanisms. Paper selection criteria involved English grammar, publication date, high citations, and broad applicability, exclusion criteria included low credibility, non-English publications, and those full-text inaccessible ones.

RESULTS

TCM and the popularity of Chinese herbal medicines (CHMs) are gaining increasing attention worldwide. This is driven, in part, by a large number of technologies, especially omics strategy, which are aiding the modernization of TCM. They contribute to the quality control of CHMs, the identification of cellular targets, discovery of new drugs and, most importantly, the understanding of their mechanisms of action.

CONCLUSION

To fully integrate TCM into modern medicine, further development of robust omics strategies is essential. This vision includes personalized medicine, backed by advanced computational power and secure data infrastructure, to facilitate global acceptance and seamless integration of TCM practices.

BAPCP: A comprehensive and user-friendly web tool for identifying biomarkers from protein microarray technologies

BACKGROUND AND OBJECTIVE

Proteome microarrays are one of the popular high-throughput screening methods for large-scale investigation of protein interactions in cells. These interactions can be measured on protein chips when coupled with fluorescence-labeled probes, helping indicate potential biomarkers or discover drugs. Several computational tools were developed to help analyze the protein chip results. However, existing tools fail to provide a user-friendly interface for biologists and present only one or two data analysis methods suitable for limited experimental designs, restricting the use cases.

METHODS

In order to facilitate the biomarker examination using protein chips, we implemented a user-friendly and comprehensive web tool called BAPCP (Biomarker Analysis tool for Protein Chip Platforms) in this research to deal with diverse chip data distributions.

RESULTS

BAPCP is well integrated with standard chip result files and includes 7 data normalization methods and 7 custom-designed quality control/differential analysis filters for biomarker extraction among experiment groups. Moreover, it can handle cost-efficient chip designs that repeat several blocks/samples within one single slide. Using experiments of the human coronavirus (HCoV) protein microarray and the E. coli proteome chip that helps study the immune response of Kawasaki disease as examples, we demonstrated that BAPCP can accelerate the time-consuming week-long manual biomarker identification process to merely 3 min.

CONCLUSIONS

The developed BAPCP tool provides substantial analysis support for protein interaction studies and conforms to the necessity of expanding computer usage and exchanging information in bioscience and medicine. The web service of BAPCP is available at https://cosbi.ee.ncku.edu.tw/BAPCP/.

De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles. In the algorithm, the substructures of multiple molecules that were selected based on the transcriptome analysis are fused in the design of new chemical structures by exploring the latent space of a Transformer-based variational autoencoder using Bayesian optimization. Our results demonstrate the usefulness of the proposed method in terms of having high reproducibility of existing ligands for 10 therapeutic target proteins when compared with previous methods. Moreover, this method can be applied to proteins without detailed 3D structures or known ligands and is expected to become a powerful tool for more efficient hit identification.

Naked-Eye Readable Microarray for Rapid Profiling of Antibodies against Multiple SARS-CoV-2 Variants

Novel high-throughput protein detection technologies are critically needed for population-based large-scale SARS-CoV-2 antibody detection as well as for monitoring quality and duration of immunity against virus variants. Current protein microarray techniques rely heavily on labeled transduction methods that require sophisticated instruments and complex operations, limiting their clinical potential, particularly for point-of-care (POC) applications. Here, we developed a label-free and naked-eye readable microarray (NRM) based on a thickness-sensing plasmon ruler, enabling antibody profiling within 30 min. The NRM chips provide 100% accuracy for neutralizing antibody detection by efficiently screening antigen types and experimental conditions and allow for the profiling of antibodies against multiple SARS-CoV-2 variants in clinical samples. We further established a flexible "barcode" NRM assay with a simple tape-based operation, enabling an effective smartphone-based readout and analysis. These results demonstrate new strategies for high-throughput protein detection and highlight the potential of novel protein microarray techniques for realistic clinical applications.

De novo drug design based on patient gene expression profiles via deep learning

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine.

Current advances in antibody-based serum biomarker studies: From protein microarray to phage display

PURPOSE

This review aims to summarize the technological advances in the field of antibody-based biomarker studies by proteome microarray and phage display. In addition, the possible development directions of this field are also discussed.

EXPERIMENTAL DESIGN

We have focused on the antibody profiling by proteome microarray and phage display, including the technological advances, the tools/resources constructed, and the characteristics of both platforms.

RESULTS

With the help of tools/resources and technological advances in proteome microarray and phage display, the efficiency of profiling antibody-based biomarkers in serum samples has been greatly improved.

CONCLUSIONS

In the past few years, proteome microarray and phage display, especially the latter one, have already demonstrated their capacity and efficiency for biomarker identification. In the near future, we believe that more antibody-based biomarkers could be identified, and some of them could eventually be developed into real clinical applications.

Common molecular mechanism and immune infiltration patterns of thoracic and abdominal aortic aneurysms

BACKGROUND

Aortic disease (aortic aneurysm (AA), dissection (AD)) is a serious threat to patient lives. Little is currently known about the molecular mechanisms and immune infiltration patterns underlying the development and progression of thoracic and abdominal aortic aneurysms (TAA and AAA), warranting further research.

METHODS

We downloaded AA (includes TAA and AAA) datasets from the GEO database. The potential biomarkers in TAA and AAA were identified using differential expression analysis and two machine-learning algorithms. The discrimination power of the potential biomarkers and their diagnostic accuracy was assessed in validation datasets using ROC curve analysis. Then, GSEA, KEGG, GO and DO analyses were conducted. Furthermore, two immuno-infiltration analysis algorithms were utilized to analyze the common immune infiltration patterns in TAA and AAA. Finally, a retrospective clinical study was performed on 78 patients with AD, and the serum from 6 patients was used for whole exome sequencing (WES).

RESULTS

The intersection of TAA and AAA datasets yielded 82 differentially expressed genes (DEGs). Subsequently, the biomarkers (CX3CR1 and HBB) were acquired by screening using two machine-learning algorithms and ROC curve analysis. The functional analysis of DEGs showed significant enrichment in inflammation and regulation of angiogenic pathways. Immune cell infiltration analysis revealed that adaptive and innate immune responses were closely linked to AA progression. However, neither CX3CR1 nor HBB was associated with B cell-mediated humoral immunity. CX3CR1 expression was correlated with macrophages and HBB with eosinophils. Finally, our retrospective clinical study revealed a hyperinflammatory environment in aortic disease. The WES study identified disease biomarkers and gene variants, some of which may be druggable.

CONCLUSION

The genes CX3CR1 and HBB can be used as common biomarkers in TAA and AAA. Large numbers of innate and adaptive immune cells are infiltrated in AA and are closely linked to the development and progression of AA. Moreover, CX3CR1 and HBB are highly correlated with the infiltration of immune cells and may be potential targets of immunotherapeutic drugs. Gene mutation research is a promising direction for the treatment of aortic disease.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Revealing Potential Diagnostic Gene Biomarkers Associated with Immune Infiltration in Patients with Renal Fibrosis Based on Machine Learning Analysis

Chronic kidney disease is characterized by the development of renal fibrosis. The basic mechanisms of renal fibrosis have not yet been fully investigated despite significant progress in understanding the etiology of the disease. In this work, the researchers sought to identify potential diagnostic indicators for renal fibrosis. From the GEO database, we were able to acquire two gene expression profiles with publically available data (GSE22459 and GSE76882, respectively) from human renal fibrosis and control samples. 215 renal fibrosis specimens and 124 normal specimens were examined for differentially expressed genes (DEGs). The SVM-RFE and LASSO regression models were used to discover potential markers. CIBERSORT was applied to estimate the combined cohorts' immune cell fraction compositional trends in renal fibrosis. RT-PCR was used to examine the expression of ISG20 in renal fibrosis and healthy samples. In vitro experiments were applied to examine the function of ISG20 knockdown on the progression of renal fibrosis. In this study, we identified 24 DEGs. The result of LASSO and SVM-RFE identified nine critical genes. ROC assays confirmed the diagnostic value of the above nine genes for renal fibrosis. Immune cell infiltration analysis revealed that ISG20 and SERPINA3 were both found to be correlated with T cell follicular helper, neutrophils, T cell CD4 memory activated, eosinophils, T cell CD8, dendritic cell activated, B cell memory, monocytes, macrophage M2, plasma cells, T cell CD4 naïve, mast cell resting, B cell naïve, T cell regulatory, and NK cell activated. Finally, we observed that the expression of ISG20 and SERPINA3 was distinctly increased in renal fibrosis samples compared with normal samples. ISG20 siRNA significantly suppressed the progression of renal fibrosis in vitro. Overall, this study identified nine diagnostic biomarkers for renal fibrosis. ISG20 may be a novel therapeutic target of renal fibrosis.

Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis

Purpose

Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients' prognosis remains poor due to tumor invasion and metastasis.

Materials and Methods

We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes.

Result

By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS.

Conclusion

In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

Heat Shock Cognate 70 kDa Protein Is the Target of Tetradecyl 2,3-Dihydroxybenzoate for Neuritogenic Effect in PC12 Cells

Tetradecyl 2,3-dihydroxybenzoate (ABG-001) is a lead compound derived from gentisides with a remarkable neuritogenic activity. However, the target of ABG-001 is yet to be defined to date. In this study, the potential target of ABG-001 was investigated via an activity-based protein profiling (ABPP) analysis, which is a chemical proteomic method for target identification by using chemical probes. Results indicated that the potential target proteins of ABG-001 were heat shock cognate 70 kDa protein (Hsc70), 78 kDa glucose-regulated protein (GRP78), and 14-3-3 theta protein. Then, the potential target of ABG-001 was confirmed by using inhibitors, the cellular thermal shift assay (CETSA) and small-interfering RNA (siRNA) analysis. The inhibitor of Hsc70 and siRNA significantly decreased the neurite outgrowth induced by ABG-001. Furthermore, ABG-001 induced neurite outgrowth was reduced by siRNA against Hsc70, and the results of CETSA suggested that Hsc70 showed a significant thermal stability-shifted effect upon ABG-001 treatment. These results indicated that Hsc70 is the target protein of ABG-001 in PC12 cells.

Neurotrophic factor levels in the serum and cerebrospinal fluid of neonates infected with human cytomegalovirus

Human cytomegalovirus (HCMV) is most likely to damage the central nervous system (CNS) during early embryonic development; however, the early neurodevelopmental abnormalities caused by HCMV infection and the regulation of cytokines remain unclear. Therefore, we investigated neuronal factors in the serum and cerebrospinal fluid (CSF) of newborns infected with HCMV using protein microarray technology with a view to elucidating the changes in specific neuronal factors for use in the development of a reliable index for predicting CNS injury caused by HCMV infection. Serum and CSF were collected from four newborns with HCMV infection and CNS injury (HCMV-infected group) and from four newborns without CNS infection (control group). A protein microarray containing 29 kinds of CNS-related cytokines was used to identify differentially expressed neuronal factors in the serum and CSF of the HCMV-infected and control groups. The levels of the differentially expressed proteins were verified further in 30 CSF samples from an HCMV-infected group using enzyme-linkedimmunosorbent assay (ELISA). Between newborns in the HCMV-infected and control groups, the protein microarray analysis identified three differentially expressed neurotrophic factors in the CSF samples: Acrp30, MMP-3, and interleukin-1 alpha (IL-1α). No differential cytokine expression was seen in the serum. ELISA showed significantly higher expression levels of Acrp30 and MMP-3 in the CSF of the 30 newborns with HCMV infection and CNS injury than in those in the control group, whereas the expression of IL-1α was significantly lower. Our results demonstrate that changes in the expression levels of Acrp30, MMP-3, and IL-1α in the CSF of newborns infected with HCMV may be related to the pathogenesis of CNS infection.

Applications of Protein Microarrays in Biomarker Discovery for Autoimmune Diseases

Dysregulated autoantibodies and cytokines were deemed to provide important cues for potential illnesses, such as various carcinomas and autoimmune diseases. Increasing biotechnological approaches have been applied to screen and identify the specific alterations of these biomolecules as distinctive biomarkers in diseases, especially autoimmune diseases. As a versatile and robust platform, protein microarray technology allows researchers to easily profile dysregulated autoantibodies and cytokines associated with autoimmune diseases using various biological specimens, mainly serum samples. Here, we summarize the applications of protein microarrays in biomarker discovery for autoimmune diseases. In addition, the key issues in the process of using this approach are presented for improving future studies.

A comprehensive screening method for investigating the potential binding targets of doxorubicin based on protein microarray

With the development of precision therapy, pharmacological research pays more and more attention to seek and confirm the target of drugs in order to understand the mechanism of drug action and reduce side effects. Screening candidate proteins can be effectively used to predict potential drug targets and toxicity. Therefore, a high-throughput drug-binding protein screening method based on protein microarray which contains over 21,000 human proteins was introduced in this investigation. Doxorubicin, a classical chemotherapeutic agent widely used in clinical treatment, was taken as a drug example in our protein screening study. Through microarray and bioinformatics analysis, more potential targets were found with different binding affinity to doxorubicin, and HRAS stands out as a critical protein from candidate proteins. In addition, the results revealed that the formation of the HRAS-RAF complex is promoted by doxorubicin. It is our expectation that the outcomes could benefit to understand the various effect of the doxorubicin and push the protein microarray screening to apply in the comprehensive pharmacological and toxicological investigation of other drugs.

Mode-of-Action of Antimicrobial Peptides: Membrane Disruption vs. Intracellular Mechanisms

Antimicrobial peptides are an attractive alternative to traditional antibiotics, due to their physicochemical properties, activity toward a broad spectrum of bacteria, and mode-of-actions distinct from those used by current antibiotics. In general, antimicrobial peptides kill bacteria by either disrupting their membrane, or by entering inside bacterial cells to interact with intracellular components. Characterization of their mode-of-action is essential to improve their activity, avoid resistance in bacterial pathogens, and accelerate their use as therapeutics. Here we review experimental biophysical tools that can be employed with model membranes and bacterial cells to characterize the mode-of-action of antimicrobial peptides.