Oxytocin-induced birth causes sex-specific behavioral and brain connectivity changes in developing rat offspring.

Despite six decades of the use of exogenous oxytocin for management of labor, little is known about its effects on the developing brain. Motivated by controversial reports suggesting a link between oxytocin use during labor and autism spectrum disorders (ASDs), we employed our recently validated rat model for labor induction with oxytocin to address this important concern. Using a combination of molecular biological, behavioral, and neuroimaging assays, we show that induced birth with oxytocin leads to sex-specific disruption of oxytocinergic signaling in the developing brain, decreased communicative ability of pups, reduced empathy-like behaviors especially in male offspring, and widespread sex-dependent changes in functional cortical connectivity. Contrary to our hypothesis, social behavior, typically impaired in ASDs, was largely preserved. Collectively, our foundational studies provide nuanced insights into the neurodevelopmental impact of birth induction with oxytocin and set the stage for mechanistic investigations in animal models and prospective longitudinal clinical studies.

Proteomic discovery of prognostic protein biomarkers for persisting problems after mild traumatic brain injury.

Some individuals with mild traumatic brain injury (mTBI), also known as concussion, have neuropsychiatric and physical problems that last longer than a few months. Symptoms following mTBI are not only impacted by the kind and severity of the injury but also by the post-injury experience and the individual's responses to it, making the persistence of mTBI particularly difficult to predict. We aimed to identify prognostic blood-based protein biomarkers predicting 6-month outcomes, in light of the clinical course after the injury, in a longitudinal mTBI cohort (N = 42). Among 420 target proteins quantified by multiple-reaction monitoring-mass spectrometry assays of blood samples, 31, 43, and 15 proteins were significantly associated with the poor recovery of neuropsychological symptoms at < 72 h, 1 week, and 1 month after the injury, respectively. Sequential associations among clinical assessments (depressive symptoms and cognitive function) affecting the 6-month outcomes were evaluated. Then, candidate biomarker proteins indirectly affecting the outcome via neuropsychological symptoms were identified. Using the identified proteins, prognostic models that can predict the 6-month outcome of mTBI were developed. These protein biomarkers established in the context of the clinical course of mTBI may have potential for clinical application.

Microglial REV-ERBα regulates inflammation and lipid droplet formation to drive tauopathy in male mice.

Alzheimer's disease, the most common age-related neurodegenerative disease, is characterized by tau aggregation and associated with disrupted circadian rhythms and dampened clock gene expression. REV-ERBα is a core circadian clock protein which also serves as a nuclear receptor and transcriptional repressor involved in lipid metabolism and macrophage function. Global REV-ERBα deletion has been shown to promote microglial activation and mitigate amyloid plaque formation. However, the cell-autonomous effects of microglial REV-ERBα in healthy brain and in tauopathy are unexplored. Here, we show that microglial REV-ERBα deletion enhances inflammatory signaling, disrupts lipid metabolism, and causes lipid droplet (LD) accumulation specifically in male microglia. These events impair microglial tau phagocytosis, which can be partially rescued by blockage of LD formation. In vivo, microglial REV-ERBα deletion exacerbates tau aggregation and neuroinflammation in two mouse tauopathy models, specifically in male mice. These data demonstrate the importance of microglial lipid droplets in tau accumulation and reveal REV-ERBα as a therapeutically accessible, sex-dependent regulator of microglial inflammatory signaling, lipid metabolism, and tauopathy.

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.

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.

A Clinically Applicable 24-Protein Model for Classifying Risk Subgroups in Pancreatic Ductal Adenocarcinomas using Multiple Reaction Monitoring-Mass Spectrometry.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) subtypes have been identified using various methodologies. However, it is a challenge to develop classification system applicable to routine clinical evaluation. We aimed to identify risk subgroups based on molecular features and develop a classification model that was more suited for clinical applications. EXPERIMENTAL DESIGN: We collected whole dissected specimens from 225 patients who underwent surgery at Seoul National University Hospital [Seoul, Republic of Korea (South)], between October 2009 and February 2018. Target proteins with potential relevance to tumor progression or prognosis were quantified with robust quality controls. We used hierarchical clustering analysis to identify risk subgroups. A random forest classification model was developed to predict the identified risk subgroups, and the model was validated using transcriptomic datasets from external cohorts (N = 700), with survival analysis. RESULTS: We identified 24 protein features that could classify the four risk subgroups associated with patient outcomes: stable, exocrine-like; activated, and extracellular matrix (ECM) remodeling. The "stable" risk subgroup was characterized by proteins that were associated with differentiation and tumor suppressors. "Exocrine-like" tumors highly expressed pancreatic enzymes. Two high-risk subgroups, "activated" and "ECM remodeling," were enriched in terms such as cell cycle, angiogenesis, immunocompetence, tumor invasion metastasis, and metabolic reprogramming. The classification model that included these features made prognoses with relative accuracy and precision in multiple cohorts. CONCLUSIONS: We proposed PDAC risk subgroups and developed a classification model that may potentially be useful for routine clinical implementations, at the individual level. This clinical system may improve the accuracy of risk prediction and treatment guidelines.See related commentary by Thakur and Singh, p. 3272.

Fully validated SRM-MS-based method for absolute quantification of PIVKA-II in human serum: Clinical applications for patients with HCC.

Protein induced by vitamin K absence or antagonist-II (PIVKA-II), an abnormal form of prothrombin, is used as a serological biomarker that aids in the diagnosis of hepatocellular carcinoma (HCC). PIVKA-II is typically measured by liquid binding assay (LiBA). However, without an internal standard, it is difficult to obtain accurate results. Thus, we aimed to develop a selected reaction monitoring-mass spectrometry (SRM-MS)-based assay to quantify PIVKA-II in serum. Our SRM-MS assay entailed the addition of a protein analog as an internal standard, the enrichment of PIVKA-II using a monoclonal antibody, chymotrypsin digestion, online desalting, and SRM-MS analysis. The performance of the SRM-MS assay was compared with that of LiBA in 400 human serum samples (100 chronic hepatitis, 100 liver cirrhosis, and 200 HCC). Integrated multinational guidelines were followed to validate the assay for clinical implementation. The linearity ranged from 1.28 to 100,000 ng/mL, and the use of a labeled protein analog as an internal standard allowed the error from the sample preparation to be corrected, improving the precision and accuracy. The SRM-MS assay was validated to meet all of the criteria of the compliance with guidelines per the US Food and Drug Administration (FDA), European medicines agency (EMA), Korea FDA (KFDA), and Clinical & Laboratory Standards Institute (CLSI). We have developed and validated a robust and reproducible SRM-MS assay that is superior to the conventional method of distinguishing HCC from noncancer patients, based on PIVKA-II levels, and satisfies clinical standards. This method has potential applications in quantifying other protein biomarkers.

Comparative proteomic analysis of human malignant ascitic fluids for the development of gastric cancer biomarkers.

OBJECTIVES: Malignant ascites is a sign of peritoneal seeding, which is one of the most frequent forms of incurable distant metastasis. Because the development of malignant ascites is associated with an extremely poor prognosis, determining whether it resulted from peritoneal seeding has critical clinical implications in diagnosis, choice of treatment, and active surveillance. At present, the molecular characterizations of malignant ascites are especially limited in case of gastric cancer. We aimed to identify malignant ascites-specific proteins that may contribute to the development of alternative methods for diagnosis and therapeutic monitoring and also increase our understanding of the pathophysiology of peritoneal seeding. DESIGN & METHODS: First, comprehensive proteomic strategies were employed to construct an in-depth proteome of ascitic fluids. Label-free quantitative proteomic analysis was subsequently performed to identify candidates that can differentiate between malignant ascitic fluilds of gastric cancer patients from benign ascitic fluids. Finally, two candidate proteins were verified by ELISA in 84 samples with gastric cancer or liver cirrhosis. RESULTS: Comprehensive proteome profiling resulted in the identification of 5347 ascites proteins. Using label-free quantification, we identified 299 proteins that were differentially expressed in ascitic fluids between liver cirrhosis and stage IV gastric cancer patients. In addition, we identified 645 proteins that were significantly expressed in ascitic fluids between liver cirrhosis and gastric cancer patients with peritoneal seeding. Finally, Gastriscin and Periostin that can distinguish malignant ascites from benign ascites were verified by ELISA. CONCLUSIONS: This study identified and verified protein markers that can distinguish malignant ascites with or without peritoneal seeding from benign ascites. Consequently, our results could be a significant resource for gastric cancer research and biomarker discovery in the diagnosis of malignant ascites.

Synthesis of ligand-selective ZnS nanocrystals exhibiting ligand-tunable fluorescence.

High-quality ZnS nanocrystals (NCs) of nearly identical size are synthesized using isomeric ligands, o-, m-, p-phenylenediamines (PDAs) that bind to the NC cores. The fluorescence emission from the NC is tunable according to the structure of the isomer. The measured fluorescence quantum yields (QYs) are 2-3 times higher for NCs that are passivated with isomeric PDA ligands than the fluorescence QY of NCs prepared at the absence of PDAs. The NC morphologies were studied by low-angle and wide-angle X-ray diffraction (XRD), and by transmission electron microscopy (TEM). The average correlating sizes were found to be 3.0+/-0.3, 3.7+/-0.30, and 3.0+/-0.5 nm for the NCs that were passivated with o-PDA, m-PDA, and p-PDA, respectively. The Fourier-transform infra-red (FTIR) spectroscopy and X-ray photoelectron spectroscopy (XPS) studies were carried out to investigate the shell structure and the interaction between the core and the shell. The adsorbed ligands were quantitatively analyzed by TGA. The structure, morphology, and optical properties of these PDA passivated NCs were compared with the NCs prepared in the absence of PDA.

Templated carbon nanofiber with mesoporosity and semiconductivity.

We have newly fabricated a long one-dimensional (1D) mesoporous carbon nanofiber by using a mesoporous silica nanofiber template. The resulting mesoporous carbon nanofiber shows the unique mesoporous structure of circularly wound nanochannel alignment perpendicular to the long fiber axis and the high gas sorption property, which interestingly presents the p-type semiconducting behavior.