Mechanisms for Improving Hepatic Glucolipid Metabolism by Cinnamic Acid and Cinnamic Aldehyde: An Insight Provided by Multi-Omics

Cinnamic acid (AC) and cinnamic aldehyde (AL) are two chemicals enriched in cinnamon and have been previously proved to improve glucolipid metabolism, thus ameliorating metabolic disorders. In this study, we employed transcriptomes and proteomes on AC and AL treated db/db mice in order to explore the underlying mechanisms for their effects. Db/db mice were divided into three groups: the control group, AC group and AL group. Gender- and age-matched wt/wt mice were used as a normal group. After 4 weeks of treatments, mice were sacrificed, and liver tissues were used for further analyses. Functional enrichment of differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. DEPs were further verified by parallel reaction monitoring (PRM). The results suggested that AC and AL share similar mechanisms, and they may improve glucolipid metabolism by improving mitochondrial functions, decreasing serotonin contents and upregulating autophagy mediated lipid clearance. This study provides an insight into the molecular mechanisms of AC and AL on hepatic transcriptomes and proteomes in disrupted metabolic situations and lays a foundation for future experiments.

Gravida Serum POSTN and PAPPA as the Potential Biomarkers for Fetal Congenital Heart Disease Based on Quantitative and Qualitative Proteomics

Background: Fetal congenital heart disease is the most common congenital defect worldwide. It has some missed diagnoses and a lack of disease biomarkers. We aim to seek objective biomarkers for noninvasive prenatal diagnosis of fetal congenital heart disease to reduce the missed diagnosis and explore its mechanism,Methods: This study used data-independent acquisition and parallel reaction monitoring to explore potential protein biomarkers that co-exist in gravida serum and fetal amniotic fluid. Moreover, logistic regression and ROC curve to establish the diagnostic model of fetal congenital heart disease potential biomarkers and molecular biology experiments were performed to validate proteomics results and explore the mechanism. Results: Proteomics and bioinformatics results show that 12 proteins co-exist in gravida serum and amniotic fluid. We identified POSTN and PAPPA in GS as candidate biomarkers and established the diagnostic model with a sensitivity of 100%, a specificity of 95% and the AUC value of 0.968 to diagnose congenital heart disease. In addition, the results of ELISA, IHC, and RT-PCR were consistent with those of proteomics. Moreover, POSTN may involve in fetal congenital heart disease through the TGF-beta signaling pathway. Conclusion: It is the first time to find that POSTN and PAPPA in GS are related to fetal congenital heart disease, contributing to developing a novel noninvasive prenatal method to diagnose fetal congenital heart disease and reduce congenital disabilities.

Comparative Proteomic Analysis of Leptospira Interrogans Serogroup Icterohaemorrhagiae Human Vaccine Strain and Epidemic Isolate of China

Background: Leptospira interrogans serogroup Icterohaemorrhagiae is the predominant pathogen causing leptospirosis in China and is still used as the vaccine strain for the current human inactivated vaccine. Unlike the clade ST17, which is distributed worldwide, ST1 is the most prevalent in serogroup Icterohaemorrhagiae in China. Purpose and Methods: To further characterize leptospiral pathogens, isobaric tags for relative and absolute quantitation and parallel reaction monitoring were used to analyze differences at the proteomic level between serogroup Icterohaemorrhagiae vaccine strain 56001 (ST1) and circulating isolate 200502 (ST17) from different periods. Results: Two hundred and eighty-one proteins were differentially expressed between ST17 and ST1, of which 166 were upregulated (>1.2 fold change, P < 0.05) and 115 (>1.2-fold change, P < 0.05) were downregulated. Function prediction revealed that nine upregulated proteins were outer membrane proteins, including several known immunogenic and/or virulence-related proteins, such as ompL1, LipL71 and LipL41. Furthermore, important expression differences in carbohydrate, amino acid, and energy metabolism and transport proteins were identified between ST1 and ST17, suggesting that these differences may reflect metabolic diversity and the potential of the pathogens to adapt to different environments. Conclusion: In summary, our findings provide insights into better understanding the component strains of the Chinese human leptospirosis vaccine at the proteomic level. Additionally, these data facilitate evaluating the mechanisms by which pathogenic Leptospira species adapt to the host environment.

Application of Parallel Reaction Monitoring in 15N labeled Samples for Quantification

Accurate relative quantification is critical in proteomic studies. The incorporation of stable isotope 15N to plant-expressed proteins in vivo is a powerful tool for accurate quantification with a major advantage of reducing preparative and analytical variabilities. However, 15N labeling quantification has several challenges. Less identifications are often observed in the heavy labeled samples because of incomplete labeling, resulting in missing values in reciprocal labeling experiments. Inaccurate quantification can happen when there is contamination from co-eluting peptides or chemical noise in the MS1 survey scan. These drawbacks in quantification can be more pronounced in less abundant but biologically interesting proteins, which often have very few identified peptides. Here we demonstrate the application of parallel reaction monitoring (PRM) to 15N labeled samples on a high resolution, high mass accuracy Orbitrap mass spectrometer to achieve reliable quantification even of low abundance proteins in samples.

Targeted proteomics as a tool to detect SARS-CoV-2 proteins in clinical specimens

The rapid, sensitive and specific detection of SARS-CoV-2 is critical in responding to the current COVID-19 outbreak. In this proof-of-concept study, we explored the potential of targeted mass spectrometry (MS) based proteomics for the detection of SARS-CoV-2 proteins in both research samples and clinical specimens. First, we assessed the limit of detection for several SARS-CoV-2 proteins by parallel reaction monitoring (PRM) MS in infected Vero E6 cells. For tryptic peptides of Nucleocapsid protein, the limit of detection was estimated to be in the mid-attomole range (9E-13 g). Next, this PRM methodology was applied to the detection of viral proteins in various COVID-19 patient clinical specimens, such as sputum and nasopharyngeal swabs. SARS-CoV-2 proteins were detected in these samples with high sensitivity in all specimens with PCR Ct values <24 and in several samples with higher CT values. A clear relationship was observed between summed MS peak intensities for SARS-CoV-2 proteins and Ct values reflecting the abundance of viral RNA. Taken together, these results suggest that targeted MS based proteomics may have the potential to be used as an additional tool in COVID-19 diagnostics.

Differential urine proteome analysis of a ventilator-induced lung injury rat model by label-free quantitative and parallel reaction monitoring proteomics

Urine is a promising resource for biomarker research. Therefore, the purpose of this study was to investigate potential urinary biomarkers to monitor the disease activity of ventilator-induced lung injury (VILI). In the discovery phase, a label-free data-dependent acquisition (DDA) quantitative proteomics method was used to profile the urinary proteomes of VILI rats. For further validation, the differential proteins were verified by parallel reaction monitoring (PRM)-targeted quantitative proteomics. In total, 727 high-confidence proteins were identified with at least 1 unique peptide (FDR ≤ 1%). Compared to the control group, 110 proteins (65 upregulated, 45 downregulated) were significantly changed in the VILI group (1.5-fold change, P < 0.05). The canonical pathways and protein–protein interaction analyses revealed that the differentially expressed proteins were enriched in multiple functions, including oxidative stress and inflammatory responses. Finally, thirteen proteins were identified as candidate biomarkers for VILI by PRM validation. Among these PRM-validated proteins, AMPN, MEP1B, LYSC1, DPP4 and CYC were previously reported as lung-associated disease biomarkers. SLC31, MEP1A, S15A2, NHRF1, XPP2, GGT1, HEXA, and ATPB were newly discovered in this study. Our results suggest that the urinary proteome might reflect the pathophysiological changes associated with VILI. These differential proteins are potential urinary biomarkers for the activity of VILI.

A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples

SARS-CoV-2, a novel human coronavirus, has created a global disease burden infecting > 100 million humans in just over a year. RT-PCR is currently the predominant method of diagnosing this viral infection although a variety of tests to detect viral antigens have also been developed. In this study, we adopted a SISCAPA-based enrichment approach using anti-peptide antibodies generated against peptides from the nucleocapsid protein of SARS-CoV-2. We developed a targeted workflow in which nasopharyngeal swab samples were digested followed by enrichment of viral peptides using the anti-peptide antibodies and targeted parallel reaction monitoring (PRM) analysis using a high-resolution mass spectrometer. This workflow was applied to 41 RT-PCR-confirmed clinical SARS-CoV-2 positive nasopharyngeal swab samples and 30 negative samples. The workflow employed was highly specific as none of the target peptides were detected in negative samples. Further, the detected peptides showed a positive correlation with the viral loads as measured by RT-PCR Ct values. The SISCAPA-based platform described in the current study can serve as an alternative method for SARS-CoV-2 viral detection and can also be applied for detecting other microbial pathogens directly from clinical samples.

Proteomic Insights Into Susceptibility and Resistance to Chronic-Stress-Induced Depression or Anxiety in the Rat Striatum

Chronic stress is a key factor for the onset of anxiety and depression disorders. However, the stress-induced common and unique molecular basis of the two psychiatric disorders is not fully known and still needs to be explored. Previously, we employed a chronic mild stress (CMS) procedure to induce a rat model including depression-susceptible (Dep-Sus), anxiety-susceptible (Anx-Sus), and insusceptible (Insus) cohorts. In this work, we continuously analyze the striatal proteomes of the three stressed cohorts by the use of comparative proteomics and bioinformatics approaches. Through isobaric tags for relative and absolute quantitation (iTRAQ)-based analysis, 386 abnormally expressed proteins in total were identified. These deregulated proteins are involved in various biological functions and significant pathways that are potentially connected with resistance and susceptibility to CMS-caused anxious- or depressive-like behaviors and, hence, could act as suggestive protein targets. A further parallel reaction monitoring-based independent investigation shows that alterations in Pak5, Dgkg, Scn4b, Rb1cc1, and Acin1; Ggps1, Fntb, Nudt19, Ufd1, and Ndufab1; and Dnajb12, Hbb2, Ap2s1, Ip6k1, and Stk4 were specifically connected with Dep-Sus, Anx-Sus, or Insus groups, respectively, potentially indicating that identical CMS treatment results in the different changes in the striatal protein regulations. Overall, our current proteomics study of the striatum provides an important molecular foundation and comprehensive insights into common and specific deregulations correlated with pathophysiological mechanisms that underlie resistance and susceptibility to chronic stress–induced anxiety or depression.