High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma.

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

TAT38 and TAT38 mimics potently inhibit adipogenesis by repressing C/EBPα, PPARγ, Pi-PPARγ, and SREBP1 expression.

Antiretroviral therapy-naive people living with HIV possess less fat than people without HIV. Previously, we found that HIV-1 transactivator of transcription (TAT) decreases fat in ob/ob mice. The TAT38 (a.a. 20-57) is important in the inhibition of adipogenesis and contains three functional domains: Cys-ZF domain (a.a. 20-35 TACTNCYCAKCCFQVC), core-domain (a.a. 36-46, FITKALGISYG), and protein transduction domain (PTD)(a.a. 47-57, RAKRRQRRR). Interestingly, the TAT38 region interacts with the Cyclin T1 of the P-TEFb complex, of which expression increases during adipogenesis. The X-ray crystallographic structure of the complex showed that the Cys-ZF and the core domain bind to the Cyclin T1 via hydrophobic interactions. To prepare TAT38 mimics with structural and functional similarities to TAT38, we replaced the core domain with a hydrophobic aliphatic amino acid (from carbon numbers 5 to 8). The TAT38 mimics with 6-hexanoic amino acid (TAT38 Ahx (C6)) and 7-heptanoic amino acid (TAT38 Ahp (C7)) inhibited adipogenesis of 3T3-L1 potently, reduced cellular triglyceride content, and decreased body weight of diet-induced obese (DIO) mice by 10.4-11 % in two weeks. The TAT38 and the TAT38 mimics potently repressed the adipogenic transcription factors genes, C/EBPα, PPARγ, and SREBP1. Also, they inhibit the phosphorylation of PPARγ. The TAT peptides may be promising candidates for development into a drug against obesity or diabetes.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study.

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Multiplexed Quantitative Proteomics Reveals Proteomic Alterations in Two Rodent Traumatic Brain Injury Models.

In many cases of traumatic brain injury (TBI), conspicuous abnormalities, such as scalp wounds and intracranial hemorrhages, abate over time. However, many unnoticeable symptoms, including cognitive, emotional, and behavioral dysfunction, often last from several weeks to years after trauma, even for mild injuries. Moreover, the cause of such persistence of symptoms has not been examined extensively. Recent studies have implicated the dysregulation of the molecular system in the injured brain, necessitating an in-depth analysis of the proteome and signaling pathways that mediate the consequences of TBI. Thus, in this study, the brain proteomes of two TBI models were examined by quantitative proteomics during the recovery period to determine the molecular mechanisms of TBI. Our results show that the proteomes in both TBI models undergo distinct changes. A bioinformatics analysis demonstrated robust activation and inhibition of signaling pathways and core proteins that mediate biological processes after brain injury. These findings can help determine the molecular mechanisms that underlie the persistent effects of TBI and identify novel targets for drug interventions.

Low Catalyst Loading Enhances Charge Accumulation for Photoelectrochemical Water Splitting.

Solar water oxidation is a critical step in artificial photosynthesis. Successful completion of the process requires four holes and releases four protons. It depends on the consecutive accumulation of charges at the active site. While recent research has shown an obvious dependence of the reaction kinetics on the hole concentrations on the surface of heterogeneous (photo)electrodes, little is known about how the catalyst density impacts the reaction rate. Using atomically dispersed Ir catalysts on hematite, we report a study on how the interplay between the catalyst density and the surface hole concentration influences the reaction kinetics. At low photon flux, where surface hole concentrations are low, faster charge transfer was observed on photoelectrodes with low catalyst density compared to high catalyst density; at high photon flux and high applied potentials, where surface hole concentrations are moderate or high, slower surface charge recombination was afforded by low-density catalysts. The results support that charge transfer between the light absorber and the catalyst is reversible; they reveal the unexpected benefits of low-density catalyst loading in facilitating forward charge transfer for desired chemical reactions. It is implied that for practical solar water splitting devices, a suitable catalyst loading is important for maximized performance.

Network analysis of plasma proteomes in affective disorders.

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

Latent class analysis of psychotic-affective disorders with data-driven plasma proteomics.

Data-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = -2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = -2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.

OH scavenging pyridinic N for facile oxygen reduction reaction on free Pt surface.

Compared to hydrogen oxidation reaction, oxygen reduction reaction (ORR) is a sluggish reaction in the proton exchange membrane fuel cell. Many studies have focused on the development of complex synthesis methods for new catalysts. In this study, we introduce a simple catalyst layer preparation method using an additive based on physical mixing for facile ORR in acid media. N-doped carbon containing mainly pyridinic N was used as the additive in the Pt or PtCo catalyst layer. The adjacent pyridinic N near the Pt catalyst works as a Lewis base and removes hydroxyl ions from Pt, thus leading to the simultaneous suppression of Pt oxidation. Based on electrochemical and spectroscopic analyses, we found that pyridinic N reacts with hydroxyl ions and is oxidized. Consequently, Pt catalyst without Pt oxide species exhibited higher ORR activity than pristine Pt or PtCo catalyst.

Proteome Multimarker Panel for the Early Detection of Hepatocellular Carcinoma: Multicenter Derivation, Validation, and Comparison.

Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case-control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring-mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both P < 0.05), test (0.894 and 0.893 vs 0.593, both P < 0.05), and confirmation sets (0.961 and 0.937 vs 0.806, both P < 0.05) in detecting small single HCC. The combination and standalone MRM-MS panels had significantly higher AUROC values than the GALAD score (0.945 and 0.931 vs 0.829, both P < 0.05). Our proteome 17-protein multimarker panel distinguished HCC patients from high-risk controls and had high accuracy in the early detection of HCC.

Comparison of Fucose-Specific Lectins to Improve Quantitative AFP-L3 Assay for Diagnosing Hepatocellular Carcinoma Using Mass Spectrometry.

Glycoproteins have many important biological functions. In particular, aberrant glycosylation has been observed in various cancers, such as liver cancer. A well-known glycoprotein biomarker is α-fetoprotein (AFP), a surveillance biomarker for hepatocellular carcinoma (HCC) that contains a glycosylation site at asparagine 251. The low diagnostic sensitivity of AFP led researchers to focus on AFP-L3, which has the same sequence as conventional AFP but contains a fucosylated glycan. AFP-L3 has high affinity for Lens culinaris agglutinin (LCA) lectin, prompting many groups to use it for detecting AFP-L3. However, a few studies have identified more effective lectins for fractionating AFP-L3. In this study, we compared the amounts of enriched AFP-L3 with five fucose-specific lectins─LCA, Lotus tetragonolobus lectin (LTL), Ulex europaeus agglutinin I (UEA I), Aleuria aurantia lectin (AAL), and Aspergillus oryzae lectin (AOL)─to identify better lectins and improve HCC diagnostic assays using mass spectrometry (MS). Our results indicate that LTL was the most effective lectin for capturing AFP-L3 species, yielding approximately 3-fold more AFP-L3 than LCA from the same pool of HCC serum samples. Thus, we recommend the use of LTL for AFP-L3 assays, given its potential to improve the diagnostic sensitivity in patients having limited results by conventional LCA assay. The MS data have been deposited to the PeptideAtlas (PASS01752).

Proteomic profiling of postmortem prefrontal cortex tissue of suicide completers.

Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography-tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.

Synergistic Effects of Fluorine and WO3 Nanoparticles on the Surface of TiO2 Hollow Spheres for Enhanced Photocatalytic Activity under Visible Light Irradiation.

TiO2 is an attractive catalyst for the photocatalytic degradation of organic pollutants. However, owing to its large band gap, it can only be activated by ultraviolet (UV) light, which constitutes a small portion of solar energy. Therefore, there has been significant interest in extending its light absorption range from UV to visible light. In this study, fluorinated TiO2 hollow spheres (FTHSs) were prepared via a rapid and simple wet chemical process using ammonium hexafluorotitanate, and then FTHS/WO3 heterostructures with different weight ratios of the FTHS and WO3 nanoparticles were synthesized via a simple wet impregnation method. The formation of the hybrid structure was confirmed by various characterization techniques. The photocatalytic activity of the synthesized photocatalysts in the photodegradation of rhodamine B, a model pollutant, was evaluated under visible light irradiation. The FTHS/WO3 heterostructures exhibited significantly improved photocatalytic activity compared to the bare FTHS or WO3 nanoparticles. The photodegradation efficiency of the FTHS/WO3 heterostructure in the present study was up to 0.0581 min-1. Detailed mechanisms that lead to the enhanced photocatalytic activity of the heterostructures are discussed. In addition, comparative experiments reveal that the photodegradation efficiency of the FTHS/WO3 heterostructure under visible light irradiation is superior to that of the P25/WO3 heterostructure prepared from the commercially available TiO2 catalyst (P25) via the same impregnation method.

Inclusive Quantification Assay of Serum Des-γ-Carboxyprothrombin Proteoforms for Hepatocellular Carcinoma Surveillance by Targeted Mass Spectrometry.

Hepatocellular carcinoma (HCC) is a malignant cancer with one of the highest mortality rates. Des-γ-carboxyprothrombin (DCP) is an HCC serologic surveillance marker that can complement the low sensitivity of alpha-fetoprotein (AFP). DCP exists in the blood as a mixture of proteoforms from an impaired carboxylation process at glutamic acid (Glu) residues within the N-terminal domain. The heterogeneity of DCP may affect the accuracy of measurements because DCP levels are commonly determined using an immunoassay that relies on antibody reactivity to an epitope in the DCP molecule. In this study, we aimed to improve the DCP measurement assay by applying a mass spectrometry (MS)-based approach for a more inclusive quantification of various DCP proteoforms. We developed a multiple-reaction monitoring-MS (MRM-MS) assay to quantify multiple noncarboxylated peptides included in the various des-carboxylation states of DCP. We performed the MRM-MS assay in 300 patients and constructed a robust diagnostic model that simultaneously monitored three noncarboxylated peptides. The MS-based quantitative assay for DCP had reliable surveillance power, which was evident from the area under the receiver operating characteristic curve (AUROC) values of 0.874 and 0.844 for the training and test sets, respectively. It was equivalent to conventional antibody-based quantification, which had AUROC values at the optimal cutoff (40 mAU/mL) of 0.743 and 0.704 for the training and test sets, respectively. The surveillance performance of the MS-based DCP assay was validated using an independent validation set consisting of 318 patients from an external cohort, resulting in an AUROC value of 0.793. Conclusion: Due to cost effectiveness and high reproducibility, the quantitative DCP assay using the MRM-MS method is superior to antibody-based quantification and has equivalent performance.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry.

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Marker Identification of the Grade of Dysplasia of Intraductal Papillary Mucinous Neoplasm in Pancreatic Cyst Fluid by Quantitative Proteomic Profiling.

The incidence of patients with pancreatic cystic lesions, particularly intraductal papillary mucinous neoplasm (IPMN), is increasing. Current guidelines, which primarily consider radiological features and laboratory data, have had limited success in predicting malignant IPMN. The lack of a definitive diagnostic method has led to low-risk IPMN patients undergoing unnecessary surgeries. To address this issue, we discovered IPMN marker candidates by analyzing pancreatic cystic fluid by mass spectrometry. A total of 30 cyst fluid samples, comprising IPMN dysplasia and other cystic lesions, were evaluated. Mucus was removed by brief sonication, and the resulting supernatant was subjected to filter-aided sample preparation and high-pH peptide fractionation. Subsequently, the samples were analyzed by LC-MS/MS. Using several bioinformatics tools, such as gene ontology and ingenuity pathway analysis, we detailed IPMNs at the molecular level. Among the 5834 proteins identified in our dataset, 364 proteins were differentially expressed between IPMN dysplasia. The 19 final candidates consistently increased or decreased with greater IPMN malignancy. CD55 was validated in an independent cohort by ELISA, Western blot, and IHC, and the results were consistent with the MS data. In summary, we have determined the characteristics of pancreatic cyst fluid proteins and discovered potential biomarkers for IPMN dysplasia.

Investigation of Electrocatalysts Produced by a Novel Thermal Spray Deposition Method.

Common methods to produce supported catalysts include impregnation, precipitation, and thermal spray techniques. Supported electrocatalysts produced by a novel method for thermal spray deposition were investigated with respect to their structural properties, elemental composition, and electrochemical performance. This was done using electron microscopy, X-ray photoelectron spectroscopy, and cyclic voltammetry. Various shapes and sizes of catalyst particles were found. The materials exhibit different activity towards oxidation and reduction of Fe. The results show that this preparation method enables the selection of particle coverage as well as size and shape of the catalyst material. Due to the great variability of support and catalyst materials accessible with this technique, this approach is a useful extension to other preparation methods for electrocatalysts.

Identification of TUBB2A by quantitative proteomic analysis as a novel biomarker for the prediction of distant metastatic breast cancer.

BACKGROUND: Metastasis of breast cancer to distal organs is fatal. However, few studies have identified biomarkers that are associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers, such as HER2, ER, and PR, to differentiate between distant and nondistant metastatic breast cancers accurately has necessitated the development of novel biomarker candidates. METHODS: An integrated proteomics approach that combined filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data from FFPE distant metastatic breast cancer tissues. A bioinformatics analysis was performed with regard to gene ontology and signaling pathways using differentially expressed proteins (DEPs) to examine the molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and function of our protein targets. RESULTS: A total of 9441 and 8746 proteins were identified from the pooled and individual sample sets, respectively. Based on our criteria, TUBB2A was selected as a novel biomarker candidate. The metastatic activities of TUBB2A were subsequently validated. In our bioinformatics analysis using DEPs, we characterized the overall molecular features of distant metastasis and measured differences in the molecular functions of distant metastatic breast cancer between breast cancer subtypes. CONCLUSIONS: Our report is the first study to examine the distant metastatic breast cancer proteome using FFPE tissues. The depth of our dataset allowed us to discover a novel biomarker candidate and a proteomic characteristics of distant metastatic breast cancer. Distinct molecular features of various breast cancer subtypes were also established. Our proteomic data constitute a valuable resource for research on distant metastatic breast cancer.

The Role of Lone-Pair Electrons in Pt-N Interactions for the Oxygen Reduction Reaction in Polymer Exchange Membrane Fuel Cells.

Invited for this month's cover is the group of Jaeyoung Lee at the Gwangju Institute of Science and Technology (GIST). The cover image shows how the lone-pair electrons on N atoms could play a role in both uniform nucleation of Pt nanoparticles and facile oxygen reduction reaction (ORR). Strong Pt-N interaction based on π-backdonation improves not only the activity of the ORR but also the durability in the polymer exchange membrane fuel cells. The Full Paper itself is available at 10.1002/cssc.201903403.

The Role of Lone-Pair Electrons in Pt-N Interactions for the Oxygen Reduction Reaction in Polymer Exchange Membrane Fuel Cells.

An N-doped carbon with various N concentrations was prepared by using a scalable ball-milling method. The importance of the lone pair of electrons on the N species for the stability of Pt nanoparticles and their activity toward the oxygen reduction reaction was investigated. X-ray spectroscopic analysis was used to investigate the interaction between Pt and the pyridinic N. The pyridinic N modified the Pt oxidation state and helped achieve size homogeneity and high catalytic activity by facilitating the rate-determining step.