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.

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.

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).

Plasma protein biomarker model for screening Alzheimer disease using multiple reaction monitoring-mass spectrometry.

Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. Yet, the complexity of diagnosing AD and measuring its invasiveness poses an obstacle. To this end, blood-based biomarkers could mitigate the inconveniences that impede an accurate diagnosis. We developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring-mass spectrometry, a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control, asymptomatic AD, prodromal AD), and AD dementia. Multimarker models were developed using 10 protein biomarkers and apolipoprotein E genotypes for amyloid beta and 10 biomarkers with Korean Mini-Mental Status Examination (K-MMSE) score for predicting Alzheimer disease progression. The accuracies for the AD classification model and AD progression monitoring model were 84.9% (95% CI 82.8 to 87.0) and 79.1% (95% CI 77.8 to 80.5), respectively. The models were more accurate in diagnosing AD, compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.