Screening of cancer-specific biomarkers for hepatitis B-related hepatocellular carcinoma based on a proteome microarray.

Hepatocellular carcinoma (HCC) is associated with one of the highest mortality rates among cancers, rendering its early diagnosis clinically invaluable. Serum biomarkers, specifically alpha-fetoprotein (AFP), represent the most promising and widely used diagnostic biomarkers for HCC. However, its detection rate is low in the early stages of HCC progression, and distinguishing specific false positives for other liver-related diseases, such as cirrhosis and acute hepatitis, remains challenging. Therefore, this study was conducted to identify biomarkers for hepatitis B (HBV)-related liver diseases by screening differentially expressed autoantibodies against tumor-associated antigens (TAAbs). We designed a large-scale multistage investigation, encompassing initial screening, HCC-focused, and ELISA validation cohorts to identify potential TAAbs in HBV-related liver diseases, spanning from healthy control (HC) individuals to patients with chronic hepatitis B (CHB), hepatitis B-related cirrhosis (HBC), and HCC, using protein microarray technology. The differential biological characteristics of TAAbs were analyzed using bioinformatics analysis. Validation of tumor-specific biomarkers for HCC was performed using ELISA. In the screening cohort, 547 candidate TAAbs were identified in the HCC group compared to those in the HC group. In the HCC-focused cohort, 64, 61, and 65 candidate TAAbs were identified in the CHB, HBC, and HCC groups, respectively, compared to those in the HC group. Thirty-four proteins exhibited continuously elevated expression from HCs to patients with CHB, HBC, and HCC. Among these, nine were identified as cancer-specific proteins. In the validation cohort, UBE2Z, CNOT3, and EID3 were correlated with liver function indicators in patients with hepatitis B-related HCC. Overall, UBE2Z, CNOT3, and EID3 emerged as cancer-specific biomarkers for HBV-related liver disease, providing a scientific basis for clinical application.

Circulating tumor-associated autoantibodies as novel diagnostic biomarkers in pancreatic adenocarcinoma.

To develop a superior diagnostic approach for pancreatic adenocarcinoma (PAAC), the present study prospectively included 338 PAAC patients, 294 normal healthy volunteers (NHV), 122 chronic pancreatitis (CP) patients and 100 patients with non-PAAC malignancies. In the identification phase, HuProt Human Proteome Microarray, comprising 21 065 proteins, was used to identify serum tumor-associated autoantibodies (TAAbs) candidates differentiating PAAC (n = 30) from NHV (n = 30). A PAAC-focused array containing 165 differentially expressed TAAbs identified was subsequently adopted in the validation phase (n = 712) for specificity and sensitivities. The multivariate TAAbs signature for differentiation PAAC from controls (NHV + CP) identified five candidates, namely the IgG-type TAAbs against CLDN17, KCNN3, SLAMF7, SLC22A11 and OR51F2. Multivariate logistic performance model of y = (22.893 × CA19-9 + 0.68 × CLDN17 - 4.012) showed a significant better diagnostic accuracy than that of CA19-9 and CLDN17 in differentiating PAAC from controls (NHV + CP) (AUC = 0.97, 0.92 and 0.82, respectively, P-value < .0001). We further tested the autoantigen level of CLDN17 by ELISA in 82 sera samples from PAAC (n = 42), CP (n = 24) and NHV (n = 16). Similarly, the model showed superior diagnostic performance than that of CA19-9 and CLDN17 (AUC = 0.93, 0.83 and 0.81, respectively, P-value < .0001) in differentiating PAAC from controls. In conclusion, our study is the first to characterize the circulating TAAbs signatures in PAAC. The results showed that CLDN17 combined with CA19-9 provided potentially clinical value and may serve as noninvasive novel biomarkers for PAAC diagnosis.

Autoantibody screening of plasma and peritoneal fluid of patients with endometriosis.

STUDY QUESTION: Are there specific autoantibody profiles in patients with endometriosis that are different from those in controls? SUMMARY ANSWER: This study did not reveal a significantly higher prevalence of autoantibodies in the studied groups of patients. WHAT IS KNOWN ALREADY: Various inflammatory factors are postulated to be involved in the pathomechanisms of endometriosis, and a potential link exists with autoimmune diseases, which may also play an important role. As the diagnosis of endometriosis remains invasive, it can only be confirmed using laparoscopy with histopathological examination of tissues. Numerous studies have focused on identifying useful biomarkers to confirm the disease, but without unequivocal effects. Autoantibodies are promising molecules that serve as potential prognostic factors. STUDY DESIGN, SIZE, DURATION: A multicentre, cross-sectional study was conducted over 18 months (between 2018 and 2019), at eight Departments of Obstetrics and Gynaecology in several cities across Poland on 137 patients undergoing laparoscopic examination for the diagnosis of endometriosis. PARTICIPANTS/MATERIALS, SETTINGS, METHODS: During laparoscopy, we obtained plasma samples from 137 patients and peritoneal fluid (PF) samples from 98 patients. Patients with autoimmune diseases were excluded from the study. Autoantibody profiling was performed using HuProt v3.1 human proteome microarrays. MAIN RESULTS AND THE ROLE OF CHANCE: We observed no significant differences in the expression of autoantibodies in the plasma or PF between the endometriosis and control groups. The study revealed that in the PF of women with Stage II endometriosis, compared with other stages, there were significantly higher reactivity signals for ANAPC15 and GABPB1 (adj. P < 0.016 and adj. P < 0.026, respectively; logFC > 1 in both cases). Comparison of the luteal and follicular phases in endometriosis patients revealed that levels of NEIL1 (adj. P < 0.029), MAGEB4 (adj. P < 0.029), and TNIP2 (adj. P < 0.042) autoantibody signals were significantly higher in the luteal phase than in the follicular phase in PF samples of patients with endometriosis. No differences were observed between the two phases of the cycle in plasma or between women with endometriosis and controls. Clustering of PF and plasma samples did not reveal unique autoantibody profiles for endometriosis; however, comparison of PF and plasma in the same patient showed a high degree of concordance. LIMITATIONS, REASONS FOR CAUTION: Although this study was performed using the highest-throughput protein array available, it does not cover the entire human proteome and cannot be used to study potentially promising post-translational modifications. Autoantibody levels depend on numerous factors, such as infections; therefore the autoantibody tests should be repeated for more objective results. WIDER IMPLICATIONS OF THE FINDINGS: Although endometriosis has been linked to different autoimmune diseases, it is unlikely that autoimmune responses mediated by specific autoantibodies play a pivotal role in the pathogenesis of this inflammatory disease. Our study shows that in searching for biomarkers of endometriosis, it may be more efficient to use higher-throughput proteomic microarrays, which may allow the detection of potentially new biomarkers. Only research on such a scale, and possibly with different technologies, can help discover biomarkers that will change the method of endometriosis diagnosis. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by a grant from the Polish Ministry of Health (grant no. 6/6/4/1/NPZ/2017/1210/1352). It was also funded by the Estonian Research Council (grant PRG1076) and the Horizon 2020 Innovation Grant (ERIN; grant no. EU952516), Enterprise Estonia (grant no. EU48695), and MSCA-RISE-2020 project TRENDO (grant no. 101008193). The authors declare that there is no conflict of interest. TRIAL REGISTRATION NUMBER: N/A.

Screening of autoantibodies as biomarkers in the serum of renal cancer patients based on human proteome microarray.

The autoantibody in patients' serum can act as a biomarker for diagnosing cancer, and the differences in autoantibodies are significantly correlated with the changes in their target proteins. In this study, 16 renal cancer (RC) patients were assigned to the disease group, and 16 healthy people were assigned to the healthy control (HC) group. The human proteome microarray consisting of>19,500 proteins was used to examine the differences in IgG and IgM autoantibodies in sera between RC and HC. The comparative analysis of the microarray results shows that 101 types of IgG and 25 types of IgM autoantibodies are significantly higher in RC than in HC. Highly responsive autoantibodies can be candidate biomarkers (e.g., anti-KCNAB2 IgG and anti-RCN1 IgM). Extensive enzyme-linked immunosorbent assay (ELISA) was performed to screen sera in 72 RC patients and 66 healthy volunteers to verify the effectiveness of the new autoantibodies. The AUCs of anti-KCNAB2 IgG and anti-GAPDH IgG were 0.833 and 0.753, respectively. KCNAB2 achieves high protein expression, and its high mRNA level is confirmed to be an unfavorable prognostic marker in clear cell renal cell carcinoma (ccRCC) tissues. This study suggests that the high-throughput human proteome microarray can effectively screen autoantibodies in serum as candidate biomarkers, and their corresponding target proteins can lay a basis for the in-depth investigation into renal cancer.

Systematic Screening of Penetratin's Protein Targets by Yeast Proteome Microarrays.

Cell-penetrating peptides (CPPs) have distinct properties to translocate across cell envelope. The key property of CPPs to translocation with attached molecules has been utilized as vehicles for the delivery of several potential drug candidates that illustrate the significant effect in in-vitro experiment but fail in in-vivo experiment due to selectively permeable nature of cell envelop. Penetratin, a well-known CPP identified from the third α-helix of Antennapedia homeodomain of Drosophila, has been widely used and studied for the delivery of bioactive molecules to treat cancers, stroke, and infections caused by pathogenic organisms. Few studies have demonstrated that penetratin directly possesses antimicrobial activities against bacterial and fungal pathogens; however, the mechanism is unknown. In this study, we have utilized the power of high-throughput Saccharomyces cerevisiae proteome microarrays to screen all the potential protein targets of penetratin. Saccharomyces cerevisiae proteome microarrays assays of penetratin followed by statistical analysis depicted 123 Saccharomyces cerevisiae proteins as the protein targets of penetratin out of ~5800 Saccharomyces cerevisiae proteins. To understand the target patterns of penetratin, enrichment analyses were conducted using 123 protein targets. In biological process: ribonucleoprotein complex biogenesis, nucleic acid metabolic process, actin filament-based process, transcription, DNA-templated, and negative regulation of gene expression are a few significantly enriched terms. Cytoplasm, nucleus, and cell-organelles are enriched terms for cellular component. Protein-protein interactions network depicted ribonucleoprotein complex biogenesis, cortical cytoskeleton, and histone binding, which represent the major enriched terms for the 123 protein targets of penetratin. We also compared the protein targets of penetratin and intracellular protein targets of antifungal AMPs (Lfcin B, Histatin-5, and Sub-5). The comparison results showed few unique proteins between penetratin and AMPs. Nucleic acid metabolic process and cellular component disassembly were the common enrichment terms for penetratin and three AMPs. Penetratin shows unique enrichment items that are related to DNA biological process. Moreover, motif enrichment analysis depicted different enriched motifs in the protein targets of penetratin, LfcinB, Histatin-5, and Sub-5.

Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis.

Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.

Systematic Identification of Protein Targets of Sub5 Using Saccharomyces cerevisiae Proteome Microarrays.

Antimicrobial peptides (AMPs) are intensively studied in terms of alternative drugs. Sub5 is a synthetic 12-mer AMP with substitutions of five amino acids of bactenecin 2A (Bac2A), a linear-ized bactenecin variant of bovine. Sub5 is highly effective against fungi with an ability to trans-locate cell membrane, but its targets are unknown. Systematic analysis of Sub5 targets will facil-itate our understanding on its mechanism of action. In this study, we used high-throughput Saccharomyces cerevisiae proteome microarrays to explore the potential protein targets of Sub5. The screening results showed 128 potential protein targets of Sub5. Bioinformatics analysis of protein targets of Sub5 revealed significant gene ontology (GO) enrichment in actin related pro-cess of "actin filament-based process", "actin filament organization", "actin cortical patch or-ganization", regulation of "actin filament bundle assembly". Moreover, the other enriched cat-egories in GO enrichment mostly contained actin associate proteins. In total, 11 actin-associated proteins were identified in the protein targets of Sub5. Protein family (PFAM) enrichment anal-ysis shows protein domain enriched in actin binding, i.e., "Cytoskeletal-regulatory complex EF hand (helix E-loop-helix F motif)". Being consistent with GO analysis, Search Tool for the Re-trieval of Interacting Genes/Proteins (STRING) analysis of the protein targets of Sub5 showed ac-tin network with involvement of 15 protein targets. Along with actin-network, STRING analysis showed protein-protein interaction network in ribonucleoprotein, transcription and translation, chromosome, histone, and ubiquitin related, DNA repair, and chaperone. Multiple Expression motifs for Motif Elicitation (MEME) suite provided a consensus binding motif of [ED][ED]EEE[ED][ED][ED][ED][ED], in total of 75 protein targets of Sub5. This motif was present in 9 out of 15 actin-related proteins identified among protein targets of Sub5.

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

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray. Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology. Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.

Plasma proteomic and autoantibody profiles reveal the proteomic characteristics involved in longevity families in Bama, China.

BACKGROUND: Chinese Bama Yao Autonomous County is a well-known longevity region in the world. In the past 30 years, population and genome studies were undertaken to investigate the secret of longevity and showed that longevity is the result of a combination of multiple factors, such as genetic, environmental and other causes. In this study, characteristics of the blood plasma proteomic and autoantibody profiles of people from Bama longevity family were investigated. METHODS: Sixty-six plasma donors from Chinese Bama longevity area were recruited in this study. Thirty-three offsprings of longevous families were selected as case studies (Longevous group) and 33 ABO (blood type), age, and gender-matched subjects from non-longevous families were selected as controls (Normal group). Each group contains 3 biological replicates. Tandem mass tag-based proteomic technique was used to investigate the differentially expressed plasma proteins between the two groups. The auto-reactive IgG antibody profiles of the 3 pooled samples in each group were revealed by human proteome microarrays with 17,000 recombinant human proteins. RESULTS: Firstly, 525 plasma proteins were quantified and 12 proteins were discovered differentially expressed between the two groups. Secondly, more than 500 proteins were recognized by plasma antibodies, 14 proteins ware differentially reacted with the autoantibodies in the two groups. Bioinformatics analysis showed some of the differential proteins and targeted autoantigens were involved in cancer, cardiovascular disease and immunity. CONCLUSIONS: Proteomic and autoantibody profiles varied between the offspring of longevous and normal families which are from the same area and shared the same environmental factors. The identified differences were reported to be involved in several physiological and pathological pathways. The identified proteins will contribute to a better understanding of the proteomic characteristics of people from Bama longevous area and a revelation of the molecular mechanisms of longevity.

Systematical Analysis of the Protein Targets of Lactoferricin B and Histatin-5 Using Yeast Proteome Microarrays.

Antimicrobial peptides (AMPs) have potential antifungal activities; however, their intracellular protein targets are poorly reported. Proteome microarray is an effective tool with high-throughput and rapid platform that systematically identifies the protein targets. In this study, we have used yeast proteome microarrays for systematical identification of the yeast protein targets of Lactoferricin B (Lfcin B) and Histatin-5. A total of 140 and 137 protein targets were identified from the triplicate yeast proteome microarray assays for Lfcin B and Histatin-5, respectively. The Gene Ontology (GO) enrichment analysis showed that Lfcin B targeted more enrichment categories than Histatin-5 did in all GO biological processes, molecular functions, and cellular components. This might be one of the reasons that Lfcin B has a lower minimum inhibitory concentration (MIC) than Histatin-5. Moreover, pairwise essential proteins that have lethal effects on yeast were analyzed through synthetic lethality. A total of 11 synthetic lethal pairs were identified within the protein targets of Lfcin B. However, only three synthetic lethal pairs were identified within the protein targets of Histatin-5. The higher number of synthetic lethal pairs identified within the protein targets of Lfcin B might also be the reason for Lfcin B to have lower MIC than Histatin-5. Furthermore, two synthetic lethal pairs were identified between the unique protein targets of Lfcin B and Histatin-5. Both the identified synthetic lethal pairs proteins are part of the Spt-Ada-Gcn5 acetyltransferase (SAGA) protein complex that regulates gene expression via histone modification. Identification of synthetic lethal pairs between Lfcin B and Histatin-5 and their involvement in the same protein complex indicated synergistic combination between Lfcin B and Histatin-5. This hypothesis was experimentally confirmed by growth inhibition assay.

Systematic identification of arsenic-binding proteins reveals that hexokinase-2 is inhibited by arsenic.

Arsenic is highly effective for treating acute promyelocytic leukemia (APL) and has shown significant promise against many other tumors. However, although its mechanistic effects in APL are established, its broader anticancer mode of action is not understood. In this study, using a human proteome microarray, we identified 360 proteins that specifically bind arsenic. Among the most highly enriched proteins in this set are those in the glycolysis pathway, including the rate-limiting enzyme in glycolysis, hexokinase-1. Detailed biochemical and metabolomics analyses of the highly homologous hexokinase-2 (HK2), which is overexpressed in many cancers, revealed significant inhibition by arsenic. Furthermore, overexpression of HK2 rescued cells from arsenic-induced apoptosis. Our results thus strongly implicate glycolysis, and HK2 in particular, as a key target of arsenic. Moreover, the arsenic-binding proteins identified in this work are expected to serve as a valuable resource for the development of synergistic antitumor therapeutic strategies.

Screening of Serum Biomarkers for Distinguishing between Latent and Active Tuberculosis Using Proteome Microarray.

OBJECTIVE: To identify potential serum biomarkers for distinguishing between latent tuberculosis infection (LTBI) and active tuberculosis (TB). METHODS: A proteome microarray containing 4,262 antigens was used for screening serum biomarkers of 40 serum samples from patients with LTBI and active TB at the systems level. The interaction network and functional classification of differentially expressed antigens were analyzed using STRING 10.0 and the TB database, respectively. Enzyme-linked immunosorbent assays (ELISA) were used to validate candidate antigens further using 279 samples. The diagnostic performances of candidate antigens were evaluated by receiver operating characteristic curve (ROC) analysis. Both antigen combination and logistic regression analysis were used to improve diagnostic ability. RESULTS: Microarray results showed that levels of 152 Mycobacterium tuberculosis (Mtb)-antigen- specific IgG were significantly higher in active TB patients than in LTBI patients (P < 0.05), and these differentially expressed antigens showed stronger associations with each other and were involved in various biological processes. Eleven candidate antigens were further validated using ELISA and showed consistent results in microarray analysis. ROC analysis showed that antigens Rv2031c, Rv1408, and Rv2421c had higher areas under the curve (AUCs) of 0.8520, 0.8152, and 0.7970, respectively. In addition, both antigen combination and logistic regression analysis improved the diagnostic ability. CONCLUSION: Several antigens have the potential to serve as serum biomarkers for discrimination between LTBI and active TB.

Systematic identification of the protein substrates of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase-T1/T2/T3 using a human proteome microarray.

O-GalNAc glycosylation is the initial step of the mucin-type O-glycosylation. In humans, it is catalyzed by a family of 20 homologous UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferases (ppGalNAc-Ts). So far, there is very limited information on their protein substrate specificities. In this study, we developed an on-chip ppGalNAc-Ts assay that could rapidly and systematically identify the protein substrates of each ppGalNAc-T. In detail, we utilized a human proteome microarray as the protein substrates and UDP-GalNAz as the nucleotide sugar donor for click chemistry detection. From a total of 16 368 human proteins, we identified 570 potential substrates of ppGalNAc-T1, T2, and T3. Among them, 128 substrates were overlapped, while the rest were isoform specific. Further cluster analysis of these substrates showed that the substrates of ppGalNAc-T1 had a closer phylogenetic relationship with that of ppGalNAc-T3 compared with ppGalNAc-T2, which was consistent with the topology of the phylogenetic tree of these ppGalNAc-Ts. Taken together, our microarray-based enzymatic assay comprehensively reveals the substrate profile of the ppGalNAc-T1, T2, and T3, which not only provides a plausible explanation for their partial functional redundancy as reported, but clearly implies some specialized roles of each enzyme in different biological processes.

Identification of Novel Efflux Proteins Rv0191, Rv3756c, Rv3008, and Rv1667c Involved in Pyrazinamide Resistance in Mycobacterium tuberculosis.

Pyrazinamide (PZA) is a critical drug used for the treatment of tuberculosis (TB). PZA is a prodrug that requires conversion to the active component pyrazinoic acid (POA) by pyrazinamidase (PZase) encoded by the pncA gene. Although resistance to PZA is mostly caused by pncA mutations and less commonly by rpsA, panD, and clpC1 mutations, clinical strains without these mutations are known to exist. While efflux of POA was demonstrated in Mycobacterium tuberculosis previously, the efflux proteins involved have not been identified. Here we performed POA binding studies with an M. tuberculosis proteome microarray and identified four efflux proteins (Rv0191, Rv3756c, Rv3008, and Rv1667c) that bind POA. Overexpression of the four efflux pump genes in M. tuberculosis caused low-level resistance to PZA and POA but not to other drugs. Furthermore, addition of efflux pump inhibitors such as reserpine, piperine, and verapamil caused increased susceptibility to PZA in M. tuberculosis strains overexpressing the efflux proteins Rv0191, Rv3756c, Rv3008, and Rv1667c. Our studies indicate that these four efflux proteins may be responsible for PZA/POA efflux and cause PZA resistance in M. tuberculosis Future studies are needed to assess their roles in PZA resistance in clinical strains.

The proteome targets of intracellular targeting antimicrobial peptides.

Antimicrobial peptides have been considered well-deserving candidates to fight the battle against microorganisms due to their broad-spectrum antimicrobial activities. Several studies have suggested that membrane disruption is the basic mechanism of AMPs that leads to killing or inhibiting microorganisms. Also, AMPs have been reported to interact with macromolecules inside the microbial cells such as nucleic acids (DNA/RNA), protein synthesis, essential enzymes, membrane septum formation and cell wall synthesis. Proteins are associated with many intracellular mechanisms of cells, thus protein targets may be specifically involved in mechanisms of action of AMPs. AMPs like pyrrhocoricin, drosocin, apidecin and Bac 7 are documented to have protein targets, DnaK and GroEL. Moreover, the intracellular targeting AMPs are reported to influence more than one protein targets inside the cell, suggesting for the multiple modes of actions. This complex mechanism of intracellular targeting AMPs makes them more difficult for the development of resistance. Herein, we have summarized the current status of AMPs in terms of their mode of actions, entry to cytoplasm and inhibition of macromolecules. To reveal the mechanism of action, we have focused on AMPs with intracellular protein targets. We have also included the use of high-throughput proteome microarray to determine the unidentified AMP protein targets in this review.

Global identification of O-GlcNAc transferase (OGT) interactors by a human proteome microarray and the construction of an OGT interactome.

O-Linked ß-N-acetylglucosamine (O-GlcNAcylation) is an important protein PTM, which is very abundant in mammalian cells. O-GlcNAcylation is catalyzed by O-GlcNAc transferase (OGT), whose substrate specificity is believed to be regulated through interactions with other proteins. There are a handful of known human OGT interactors, which is far from enough for fully elucidating the substrate specificity of OGT. To address this challenge, we used a human proteome microarray containing ~17,000 affinity-purified human proteins to globally identify OGT interactors and identified 25 OGT-binding proteins. Bioinformatics analysis showed that these interacting proteins play a variety of roles in a wide range of cellular functions and are highly enriched in intra-Golgi vesicle-mediated transport and vitamin biosynthetic processes. Combining newly identified OGT interactors with the interactors identified prior to this study, we have constructed the first OGT interactome. Bioinformatics analysis suggests that the OGT interactome plays important roles in protein transportation/localization and transcriptional regulation. The novel OGT interactors that we identified in this study could serve as a starting point for further functional analysis. Because of its high-throughput and parallel analysis capability, we strongly believe that protein microarrays could be easily applied for the global identification of regulators for other key enzymes.

Global identification of CobB interactors by an Escherichia coli proteome microarray.

Protein acetylation is one of the most abundant post-translational modifications and plays critical roles in many important biological processes. Based on the recent advances in mass spectrometry technology, in bacteria, such as Escherichia coli, tremendous acetylated proteins and acetylation sites have been identified. However, only one protein deacetylase, i.e. CobB, has been identified in E. coli so far. How CobB is regulated is still elusive. One right strategy to study the regulation of CobB is to globally identify its interacting proteins. In this study, we used a proteome microarray containing ∼4000 affinity-purified E. coli proteins to globally identify CobB interactors, and finally identified 183 binding proteins of high stringency. Bioinformatics analysis showed that these interacting proteins play a variety of roles in a wide range of cellular functions and are highly enriched in carboxylic acid metabolic process and hexose catabolic process, and also enriched in transferase and hydrolase. We further used bio-layer interferometry to analyze the interaction and quantify the kinetic parameters of putative CobB interactors, and clearly showed that CobB could strongly interact with TopA and AccC. The novel CobB interactors that we identified could serve as a start point for further functional analysis.

Proteome microarray identifies autoantibody biomarkers for diagnosis of hepatitis B-related hepatocellular carcinoma.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) has the highest mortality rate among malignant tumors worldwide. This study aimed to analyze the biological characteristics of serum proteins in hepatitis B (HBV)-related liver diseases, identify diagnostic biomarkers for HBV-infected HCC, and provide a scientific basis for its prevention and treatment. MATERIALS AND METHODS: We used HuProt arrays to identify candidate biomarkers for HBV-related liver diseases and verified the differential biomarkers by using an HCC-focused array. The biological characteristics of serum proteins were analyzed via bioinformatics. Serum biomarkers levels were validated by ELISA. RESULTS: We identified 547 differentially expressed proteins from HBV-infected HCC in a screening cohort. After analyzing the biological characteristics of serum proteins, we identified 10 potential differential autoantibodies against tumor-associated antigens (TAAbs) and a candidate biomarker panel (APEX2, RCSD1, and TP53) for the diagnosis of HBV-associated HCC with 61.9% sensitivity and 81.7% specificity in an HCC-focused array validation cohort. Finally, the protein levels and diagnostic capability of the biomarker panel were confirmed in a large-sample validation cohort, and this panel was found to be superior to alpha-fetoprotein, the standard hallmark for the diagnosis of HCC. CONCLUSION: The APEX2, RCSD1, and TP53 biomarker panels could be used for the diagnosis of HBV-associated HCC, providing a scientific basis for clinical practice.

Human Proteome Microarray identifies autoantibodies to tumor-associated antigens as serological biomarkers for the diagnosis of hepatocellular carcinoma.

The identification of the high-efficiency and non-invasive biomarkers for hepatocellular carcinoma (HCC) detection is urgently needed. This study aims to screen out potential autoantibodies to tumor-associated antigens (TAAbs) and to assess their diagnostic value for HCC. Fifteen potential TAAbs were screened out from the Human Proteome Microarray by 30 HCC sera and 22 normal control sera, of which eight passed multiple-stage validations by ELISA with a total of 1625 human serum samples from normal controls (NCs) and patients with HCC, liver cirrhosis, chronic hepatitis B, gastric cancer, esophageal cancer, and colorectal cancer. Finally, an immunodiagnostic model including six TAAbs (RAD23A, CAST, RUNX1T1, PAIP1, SARS, PRKCZ) was constructed by logistic regression, and yielded the area under curve (AUC) of 0.835 and 0.788 in training and validation sets, respectively. The serial serum samples from HCC model mice were tested to explore the change in TAAbs during HCC formation, and an increasing level of autoantibodies was observed. In conclusion, the panel of six TAAbs can provide potential value for HCC detection, and the strategy to identify novel serological biomarkers can also provide new clues in understanding immunodiagnostic biomarkers.

Using Proteome Microarray and Gene Expression Omnibus Database to Screen Tumour-Associated Antigens to Construct the Optimal Diagnostic Model of Oesophageal Squamous Cell Carcinoma.

AIMS: Autoantibodies against tumour-associated antigens (TAAs) are promising biomarkers for early immunodiagnosis of cancers. This study was designed to screen and verify autoantibodies against TAAs in sera as diagnostic biomarkers for oesophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: The customised proteome microarray based on cancer driver genes and the Gene Expression Omnibus database were used to identify potential TAAs. The expression levels of the corresponding autoantibodies in serum samples obtained from 243 ESCC patients and 243 healthy controls were investigated by enzyme-linked immunosorbent assay (ELISA). In total, 486 serum samples were randomly divided into the training set and the validation set in the ratio of 2:1. Logistic regression analysis, recursive partition analysis and support vector machine were performed to establish different diagnostic models. RESULTS: Five and nine candidate TAAs were screened out by proteome microarray and bioinformatics analysis, respectively. Among these 14 anti-TAAs autoantibodies, the expression level of nine (p53, PTEN, GNA11, SRSF2, CXCL8, MMP1, MSH6, LAMC2 and SLC2A1) anti-TAAs autoantibodies in the cancer patient group was higher than that in the healthy control group based on the results from ELISA. In the three constructed models, a logistic regression model including four anti-TAA autoantibodies (p53, SLC2A1, GNA11 and MMP1) was considered to be the optimal diagnosis model. The sensitivity and specificity of the model in the training set and the validation set were 70.4%, 72.8% and 67.9%, 67.9%, respectively. The area under the receiver operating characteristic curve for detecting early patients in the training set and the validation set were 0.84 and 0.85, respectively. CONCLUSIONS: This approach to screen novel TAAs is feasible, and the model including four autoantibodies could pave the way for the diagnosis of ESCC.