Genetic regulation of mouse liver metabolite levels.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome-wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co-variation across various biological scales

Production status and research advancement on root rot disease of faba bean (Vicia faba L.) in China

China is the largest producer of faba bean with a total harvested area of 8.11×105 ha and a total production of 1.69 ×106 tons (dry beans) in 2020, accounting for 30% of the world production. Faba bean is grown in China for both fresh pods and dry seed. East China cultivates large seed cultivars for food processing and fresh vegetables, while northwestern and southwestern China grow cultivars for dry seeds, with an increased production of fresh green pods. Most of the faba bean is consumed domestically, with limited exports. The absence of unified quality control measures and simple traditional cultivation practices contributes to the lower competitiveness of the faba bean industry in international markets. Recently, new cultivation methods have emerged with improved weed control, as well as better water and drainage management, resulting in higher quality and income for producers. Root rot disease in faba bean is caused by multiple pathogens, including Fusarium spp., Rhizoctonia spp., and Pythium spp. Fusarium spp. is the most prevalent species causing root rot in faba bean crops and is responsible for severe yield loss, with different species causing the disease in different regions in China. The yield loss ranges from 5% to 30%, up to 100% in severely infected fields. The management of faba bean root rot disease in China involves a combination of physical, chemical, and bio-control methods, including intercropping with non-host crops, applying rational nitrogen, and treating seeds with chemical or bio-seed treatments. However, the effectiveness of these methods is limited due to the high cost, the broad host range of the pathogens, and potential negative impacts on the environment and non-targeted soil organisms. Intercropping is the most widely utilized and economically friendly control method to date. This review provides an overview of the current status of faba bean production in China, the challenges faced by the industry due to root rot disease, and the progress in identifying and managing this disease. This information is critical for developing integrated management strategies to effectively control root rot in faba bean cultivation and facilitating the high-quality development of the faba bean industry

Identification and validation of G protein-coupled receptors modulating flow-dependent signaling pathways in vascular endothelial cells

Vascular endothelial cells are exposed to mechanical forces due to their presence at the interface between the vessel wall and flowing blood. The patterns of these mechanical forces (laminar vs. turbulent) regulate endothelial cell function and play an important role in determining endothelial phenotype and ultimately cardiovascular health. One of the key transcriptional mediators of the positive effects of laminar flow patterns on endothelial cell phenotype is the zinc-finger transcription factor, krüppel-like factor 2 (KLF2). Given its importance in maintaining a healthy endothelium, we sought to identify endothelial regulators of the KLF2 transcriptional program as potential new therapeutic approaches to treating cardiovascular disease. Using an approach that utilized both bioinformatics and targeted gene knockdown, we identified endothelial GPCRs capable of modulating KLF2 expression. Genetic screening using siRNAs directed to these GPCRs identified 12 potential GPCR targets that could modulate the KLF2 program, including a subset capable of regulating flow-induced KLF2 expression in primary endothelial cells. Among these targets, we describe the ability of several GPCRs (GPR116, SSTR3, GPR101, LGR4) to affect KLF2 transcriptional activation. We also identify these targets as potential validated targets for the development of novel treatments targeting the endothelium. Finally, we highlight the initiation of drug discovery efforts for LGR4 and report the identification of the first known synthetic ligands to this receptor as a proof-of-concept for pathway-directed phenotypic screening to identify novel drug targets

Salivary gland stem cells age prematurely in primary Sjögren's syndrome

Objective A major characteristic of the autoimmune disease primary Sjögren's syndrome (SS) is salivary gland (SG) hypofunction. The inability of resident SG stem cells (SGSCs) to maintain homeostasis and saliva production has never been explained and limits our comprehension of mechanisms underlying primary SS. The present study was undertaken to investigate the role of salivary gland stem cells in hyposalivation in primary SS. Methods SGSCs were isolated from parotid biopsy samples from controls and patients classified as having primary SS or incomplete primary SS, according to the American College of Rheumatology/European League Against Rheumatism criteria. Self‐renewal and differentiation assays were used to determine SGSC regenerative potential, RNA was extracted for sequencing analysis, single telomere length analysis was conducted to determine telomere length, and frozen tissue samples were used for immunohistochemical analysis. Results SGSCs isolated from primary SS parotid gland biopsy samples were regeneratively inferior to healthy control specimens. We demonstrated that SGSCs from samples from patients with primary SS are not only lower in number and less able to differentiate, but are likely to be senescent, as revealed by telomere length analysis, RNA sequencing, and immunostaining. We further found that SGSCs exposed to primary SS–associated proinflammatory cytokines we induced to proliferate, express senescence‐associated genes, and subsequently differentiate into intercalated duct cells. We also localized p16+ senescent cells to the intercalated ducts in primary SS SG tissue, suggesting a block in SGSC differentiation into acinar cells. Conclusion This study represents the first characterization of SGSCs in primary SS, and also the first demonstration of a linkage between an autoimmune disease and a parenchymal premature‐aging phenotype. The knowledge garnered in this study indicates that disease‐modifying antirheumatic drugs used to treat primary SS are not likely to restore saliva production, and should be supplemented with fresh SGSCs to recover saliva production

Genetic Control of Obesity and Gut Microbiota Composition in Response to High-Fat, High-Sucrose Diet in Mice

Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that is not accounted for by food intake and provide evidence for a genetically determined set-point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity

Transcriptional Profiling of the Dose Response: A More Powerful Approach for Characterizing Drug Activities

The dose response curve is the gold standard for measuring the effect of a drug treatment, but is rarely used in genomic scale transcriptional profiling due to perceived obstacles of cost and analysis. One barrier to examining transcriptional dose responses is that existing methods for microarray data analysis can identify patterns, but provide no quantitative pharmacological information. We developed analytical methods that identify transcripts responsive to dose, calculate classical pharmacological parameters such as the EC50, and enable an in-depth analysis of coordinated dose-dependent treatment effects. The approach was applied to a transcriptional profiling study that evaluated four kinase inhibitors (imatinib, nilotinib, dasatinib and PD0325901) across a six-logarithm dose range, using 12 arrays per compound. The transcript responses proved a powerful means to characterize and compare the compounds: the distribution of EC50 values for the transcriptome was linked to specific targets, dose-dependent effects on cellular processes were identified using automated pathway analysis, and a connection was seen between EC50s in standard cellular assays and transcriptional EC50s. Our approach greatly enriches the information that can be obtained from standard transcriptional profiling technology. Moreover, these methods are automated, robust to non-optimized assays, and could be applied to other sources of quantitative data

Finite Difference/Fourier Spectral for a Time Fractional Black–Scholes Model with Option Pricing

We study the fractional Black–Scholes model (FBSM) of option pricing in the fractal transmission system. In this work, we develop a full-discrete numerical scheme to investigate the dynamic behavior of FBSM. The proposed scheme implements a known L1 formula for the α-order fractional derivative and Fourier-spectral method for the discretization of spatial direction. Energy analysis indicates that the constructed discrete method is unconditionally stable. Error estimate indicates that the 2−α-order formula in time and the spectral approximation in space is convergent with order OΔt2−α+N1−m, where m is the regularity of u and Δt and N are step size of time and degree, respectively. Several numerical results are proposed to confirm the accuracy and stability of the numerical scheme. At last, the present method is used to investigate the dynamic behavior of FBSM as well as the impact of different parameters

Characteristics of extreme precipitation and its sensitivity to regional climate change in the upper and middle reaches of the Yellow River Basin

[Objective] Clarifying the regional characteristics and variation trends of extreme precipitation events has great significance for ecological security and disaster mitigation under climate change. [Methods] Based on the observation data from 1961-2020, linear trend analysis, M-K test, Morlet wavelet analysis, and correlation analysis were used to analyze the spatiotemporal variation of extreme precipitation events and their sensitivity to climate change from previous period (1961-1990) and recent period (1991-2020) across the upper and middle Yellow River Basin. [Results] (1) Most extreme precipitation indices decreased first and then increased around the 1990s, except the continuously decreasing consecutive dry days (CDD). In the recent period, average daily rainfall intensity (SDII), rainfall on very wet days (R95), and rainstorm days (R50) significantly rose at 0.43 mm/(d·10a), 13.98 mm/10a, and 0.06 d/10a respectively (p < 0.05). (2) In the whole period, the southwestern part of the study region was relatively wet while the Yellow River bend area was the driest, and the extreme heavy precipitation presented more in the southeast and less in the west. In the recent period, the wetting trend in the upper Yellow River Basin gradually increased, and the frequency and intensity of extreme heavy precipitation in the middle Yellow River Basin increased significantly. (3) The average annual temperature in the upper and middle Yellow River Basin rose by about 1.5 ℃ with the acceleration of the warming rate during the past 60 years. The annual precipitation first decreased and then increased, and the upward trend in the recent period reached the extremely significant level (p < 0.01). The climate of the study region was transforming from warm-dry to warm-wet, especially in the upper part of the basin. Extreme precipitation was more sensitive to the annual precipitation amount than average temperature and had significant positive correlations, except for CDD. [Conclusion] The trends and magnitudes of variation of extreme precipitation events in the previous period and the recent period were much larger than that in the whole period. Since the 1990s, distinct warm-wet trend appeared in the upper reaches, while extreme heavy precipitation events increased significantly in the middle Yellow River Basin, requiring special attention to future floods

Accuracy and Error Sources of the Rietveld Full Pattern Fitting Method in Quantitative Analysis of Illite Ores

BACKGROUND: Illite is an important mineral resource. It is of great theoretical and practical significance to accurately obtain the mineral composition and content of illite ores. The Rietveld full pattern fitting method uses the whole diffraction pattern for analysis and shows high accuracy. However, due to the lack of pure illite samples, the accuracy and error sources of this method for analyzing the content of illite ores are not clearly known at present.OBJECTIVES: To understand the accuracy and error sources of the Rietveld full pattern fitting method in quantitative analysis of illite ores.METHODS: The Rietveld full pattern fitting method was used to quantitatively analyze artificial and natural illite ore samples. The results of X-ray fluorescence spectrometry (XRF) of natural illite ores were compared with the chemical compositions calculated from the mineral contents by the Rietveld method.RESULTS: The results showed that the maximum absolute error ranges of illite-quartz binary mixtures, illite-quartz-albite ternary mixtures, and illite-quartz-albite-calcite-kaolinite multiple mixtures were -0.9%-0.9%, -1.9%-1.6%, and -2.3%-1.6%, respectively. The chemical compositions of natural illite ores calculated by Rietveld method were in good agreement with the results of XRF. This indicated that the Rietveld method had high accuracy in the quantitative analysis of mineral contents of natural illite ore samples. The error sources were mainly affected by the illite structural model, atomic thermal displacement parameters Uiso, and preferred orientation.CONCLUSIONS: A reasonable structural model of illite should be chosen according to the actual samples. The values of atomic thermal vibration Uiso should be reasonably set according to the references. Preferred orientation should be decreased as much as possible during the sample preparation