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The Keap1-Nrf2 system is central for mammalian cytoprotection against various stresses and a drug target for disease prevention and treatment. One model for the molecular mechanisms leading to Nrf2 activation is the Hinge-Latch model, where the DLGex-binding motif of Nrf2 dissociates from Keap1 as a latch, while the ETGE motif remains attached to Keap1 as a hinge. To overcome the technical difficulties in examining the binding status of the two motifs during protein-protein interaction (PPI) simultaneously, we utilized NMR spectroscopy titration experiments. Our results revealed that latch dissociation is triggered by low-molecular-weight Keap1-Nrf2 PPI inhibitors and occurs during p62-mediated Nrf2 activation, but not by electrophilic Nrf2 inducers. This study demonstrates that Keap1 utilizes a unique Hinge-Latch mechanism for Nrf2 activation upon challenge by non-electrophilic PPI-inhibiting stimuli, and provides critical insight for the pharmacological development of next-generation Nrf2 activators targeting the Keap1-Nrf2 PPI.Using NMR spectroscopy, Horie, Suzuki, Inoue et al. show that the dissociation of Keap1 from Nrf2, or the Hinge-Latch mechanism, is triggered by Keap1-Nrf2 inhibitors and occurs during p62- mediated Nrf2 activation, but not by electrophilic Nrf2 inducers. This study provides insights into the design of Nrf2 activators targeting the Keap1-Nrf2 interaction.

Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens' pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

Essential trace elements, such as iron (Fe) and selenium (Se), play physiological roles in our body, whereas environmental toxic metals, such as arsenic (As), cadmium (Cd), and mercury (Hg), are known to be associated with various disease risks. However, the relationship between elements, biochemical parameters, and lifestyle habits based on multi-elemental analysis in healthy individuals has not been fully verified. Multi-elemental analysis is useful for evaluating the change in the concentration of these elements and metals. In the present study using totally 100 µL plasma samples from the Tohoku Medical Megabank (TMM) community-based cohort study (total of 506 specimens), we conducted a multi-elemental analysis to evaluate 14 elements in generally healthy subjects. We further determined Se-transporter selenoprotein P levels using the originally developed ELISA method, since increases and decreases in selenoprotein P levels are associated with various disease risks. Multiple correlation analyses between the obtained measured values and several factors suggest that elements such as Fe, Se, and Hg, as well as selenoprotein P levels, are associated with gender differences. We also found that factors such as Fe, Se, As, Hg, hematocrit value, hemoglobin (Hb) content, and HbA1c are correlated with selenoprotein P levels. Furthermore, correlations between Fe levels and Hb content and between As/Hg and fish consumption were found. These findings demonstrate the suitability of multi-elemental analyses with limited plasma sample amounts, clearly show gender-differentiated elements, and establish a significant relationship between selenoprotein P and Fe metabolism.

The oomycete pathogen Phytophthora infestans causes potato late blight, one of the most economically damaging plant diseases worldwide. P. infestans produces AVR3a, an essential modular virulence effector with an N-terminal RXLR domain that is required for host-cell entry. In host cells, AVR3a stabilizes and inhibits the function of the E3 ubiquitin ligase CMPG1, a key factor in host immune responses including cell death triggered by the pathogen-derived elicitor protein INF1 elicitin. To elucidate the molecular basis of AVR3a effector function, we determined the structure of Phytophthora capsici AVR3a4, a close homolog of P. infestans AVR3a. Our structural and functional analyses reveal that the effector domain of AVR3a contains a conserved, positively charged patch and that this region, rather than the RXLR domain, is required for binding to phosphatidylinositol monophosphates (PIPs) in vitro. Mutations affecting PIP binding do not abolish AVR3a recognition by the resistance protein R3a but reduce its ability to suppress INF1-triggered cell death in planta. Similarly, stabilization of CMPG1 in planta is diminished by these mutations. The steady-state levels of non–PIP-binding mutant proteins in planta are reduced greatly, although these proteins are stable in vitro. Furthermore, overexpression of a phosphatidylinositol phosphate 5-kinase results in reduction of AVR3a levels in planta. Our results suggest that the PIP-binding ability of the AVR3a effector domain is essential for its accumulation inside host cells to suppress CMPG1-dependent immunity.

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

Purpose To explore the frequency and positions of genetic mutations in CYP1B1 and FOXC1 in a Japanese population. Study design Molecular genetic analysis. Methods Genomic DNA was extracted from 31 Japanese patients with childhood glaucoma (CG) from 29 families. We examined the CYP1B , FOXC1 , and MYOC genes using Sanger sequencing and whole-exome sequencing (WES). Results For CYP1B1 , we identified 9 families that harbored novel mutations, p.A202T, p.D274E, p.Q340*, and p.V420G; the remaining mutations had been previously reported. When mapped to the CYP1B1 protein structure, all mutations appeared to influence the enzymatic activity of CYP1B1 by provoking structural deformity. Five patients were homozygotes or compound heterozygotes, supporting the recessive inheritance of the CYP1B1 mutations in CG. In contrast, four patients were heterozygous for the CYP1B1 mutation, suggesting the presence of regulatory region mutations or strong modifiers. For the FOXC1 gene, we identified 3 novel mutations, p.Q23fs, p.Q70R, and p.E163*, all of which were identified in a heterozygous state. No mutation was found in the MYOC gene in these CG patients. All individuals with CYP1B1 and FOXC1 mutations were severely affected by early-onset CG. In the CYP1B1- , FOXC1- , and MYOC- negative families, we also searched for variants in the other candidate genes reported for CG through WES, but could not find any mutations in these genes. Conclusions Our analyses of 29 CG families revealed 9 families with point mutations in the CYP1B1 gene, and four of those patients appeared to be heterozygotes, suggesting the presence of complex pathogenic mechanisms. FOXC1 appears to be another major causal gene of CG, indicating that panel sequencing of CYP1B1 and FOXC1 will be useful for diagnosis of CG in Japanese individuals.

Cancer is a systemic disease rather than a localized pathology and is characterized by widespread effects, including whole-body exhaustion and chronic inflammation. A thorough understanding of cancer pathophysiology requires a systemic approach that accounts for the complex interactions between cancer cells and host tissues. To explore these dynamics, we employed a comprehensive metabolomic analysis of plasma samples from patients with either esophageal or head and neck squamous cell carcinoma (SCC). Plasma samples from 149 patients were metabolically profiled and correlated with clinical data. Among the metabolites identified, lysophosphatidylcholine (LPC) emerged as the sole biomarker strongly correlated with prognosis. A significant reduction in plasma LPC levels was linked to poorer overall survival. Plasma LPC levels demonstrated minimal correlation with patient-specific factors, such as tumor size and general condition, but showed significant association with the response to immune checkpoint inhibitor therapy. Proteomic and cytokine analyses revealed that low plasma LPC levels reflected systemic chronic inflammation, characterized by high levels of inflammatory proteins, the cytokines interleukin-6 and tumor necrosis factor-α, and coagulation-related proteins. These findings indicate that plasma LPC levels may be used as reliable biomarkers for predicting prognosis and evaluating the efficacy of immunotherapy in patients with SCC.

Chronic kidney disease (CKD) is a syndrome characterized by a gradual loss of kidney function with decreased estimated glomerular filtration rate (eGFR), which may be accompanied by an increase in the urine albumin-to-creatinine ratio (UACR). Although trans-ethnic genome-wide association studies (GWASs) have been conducted for kidney-related traits, there have been few analyses in the Japanese population, especially for the UACR trait. In this study, we conducted a GWAS to identify loci related to multiple kidney-related traits in Japanese individuals. First, to detect loci associated with CKD, eGFR, and UACR, we performed separate GWASs with the following two datasets: 475 cases of CKD diagnosed at seven university hospitals and 3471 healthy subjects (dataset 1) and 3664 cases of CKD-suspected individuals with eGFR <60 ml/min/1.73 m2 or urinary protein ≥ 1+ and 5952 healthy subjects (dataset 2). Second, we performed a meta-analysis between these two datasets and detected the following associated loci: 10 loci for CKD, 9 loci for eGFR, and 22 loci for UACR. Among the loci detected, 22 have never been reported previously. Half of the significant loci for CKD were shared with those for eGFR, whereas most of the loci associated with UACR were different from those associated with CKD or eGFR. The GWAS of the Japanese population identified novel genetic components that were not previously detected. The results also suggest that the group primarily characterized by increased UACR possessed genetically different features from the group characterized by decreased eGFR.

Signal overlapping is a major bottleneck for protein NMR analysis. We propose a new method, stable-isotope-assisted parameter extraction (SiPex), to resolve overlapping signals by a combination of amino-acid selective isotope labeling (AASIL) and tensor decomposition. The basic idea of Sipex is that overlapping signals can be decomposed with the help of intensity patterns derived from quantitative fractional AASIL, which also provides amino-acid information. In SiPex, spectra for protein characterization, such as 15N relaxation measurements, are assembled with those for amino-acid information to form a four-order tensor, where the intensity patterns from AASIL contribute to high decomposition performance even if the signals share similar chemical shift values or characterization profiles, such as relaxation curves. The loading vectors of each decomposed component, corresponding to an amide group, represent both the amino-acid and relaxation information. This information link provides an alternative protein analysis method that does not require “assignments” in a general sense; i.e., chemical shift determinations, since the amino-acid information for some of the residues allows unambiguous assignment according to the dual selective labeling. SiPex can also decompose signals in time-domain raw data without Fourier transform, even in non-uniformly sampled data without spectral reconstruction. These features of SiPex should expand biological NMR applications by overcoming their overlapping and assignment problems.

Epithelial ovarian cancer (EOC) is diagnosed at an advanced stage in over half of the patients and its prognosis remains unfavorable. CA125, one of the most frequent positive tumor markers in patients with EOC, has certain limitations. Therefore, more accurate clinical biomarkers are needed. Liquid biopsy with cancer related molecules, such as circulating tumor DNA, is a new option for cancer diagnosis and prognosis. We explored the potential of plasma metabolomic and proteomic analyses as novel monitoring methods for the patients with EOC. Of seven patients, six experienced disease recurrence or progression. CA125 plasma measurements were conducted for disease monitoring. Plasma metabolome and proteome analyses were performed using liquid chromatography–tandem mass spectrometry. Ten and four metabolome indicators were significantly increased and decreased in association with chemotherapeutic resistance, respectively. In addition, thirty-seven and nine proteins displayed high and low levels associated with chemotherapeutic resistance, respectively. Several metabolome pathways and protein concentrations corresponded to the clinical course of each patient. This pilot study suggested the potential of the assessment of metabolome and proteome analysis as a useful tool for developing novel monitoring biomarkers for patients with recurrent EOC.