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Abstract Background Biotransformation of CO2 into high-value-added carbon-based products is a promising process for reducing greenhouse gas emissions. To realize the green transformation of CO2, we use fatty acids as carbon source to drive CO2 fixation to produce succinate through a portion of the 3-hydroxypropionate (3HP) cycle in Cupriavidus necator H16. Results This work can achieve the production of a single succinate molecule from one acetyl-CoA molecule and two CO2 molecules. It was verified using an isotope labeling experiment utilizing NaH13CO3. This implies that 50% of the carbon atoms present in succinate are derived from CO2, resulting in a twofold increase in efficiency compared to prior methods of succinate biosynthesis that relied on the carboxylation of phosphoenolpyruvate or pyruvate. Meanwhile, using fatty acid as a carbon source has a higher theoretical yield than other feedstocks and also avoids carbon loss during acetyl-CoA and succinate production. To further optimize succinate production, different approaches including the optimization of ATP and NADPH supply, optimization of metabolic burden, and optimization of carbon sources were used. The resulting strain was capable of producing succinate to a level of 3.6 g/L, an increase of 159% from the starting strain. Conclusions This investigation established a new method for the production of succinate by the implementation of two CO2 fixation reactions and demonstrated the feasibility of ATP, NADPH, and metabolic burden regulation strategies in biological carbon fixation.

Abstract Background Abnormal lipid metabolism poses a risk for prediabetes. However, research on lipid parameters used to predict the risk of prediabetes is scarce, and the significance of traditional and untraditional lipid parameters remains unexplored in prediabetes. This study aimed to comprehensively evaluate the association between 12 lipid parameters and prediabetes and their diagnostic value. Methods This cross-sectional study included data from 100,309 Chinese adults with normal baseline blood glucose levels. New onset of prediabetes was the outcome of concern. Untraditional lipid parameters were derived from traditional lipid parameters. Multivariate logistic regression and smooth curve fitting were used to examine the nonlinear relationship between lipid parameters and prediabetes. A two-piecewise linear regression model was used to identify the critical points of lipid parameters influencing the risk of prediabetes. The areas under the receiver operating characteristic curve estimated the predictive value of the lipid parameters. Results A total of 12,352 participants (12.31%) were newly diagnosed with prediabetes. Following adjustments for confounding covariables, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol were negatively correlated with prediabetes risk. Conversely, total cholesterol, triglyceride (TG), lipoprotein combine index (LCI), atherogenic index of plasma (AIP), non-HDL-C, atherogenic coefficient, Castelli’s index-I, remnant cholesterol (RC), and RC/HDL-C ratio displayed positive correlations. In younger adults, females, individuals with a family history of diabetes, and non-obese individuals, LCI, TG, and AIP exhibited higher predictive values for the onset of prediabetes compared to other lipid profiles. Conclusion Nonlinear associations were observed between untraditional lipid parameters and the risk of prediabetes. The predictive value of untraditional lipid parameters for prediabetes surpassed that of traditional lipid parameters, with LCI emerging as the most effective predictor for prediabetes.

Abstract Background Visceral adiposity index (VAI) has been recognized as a reliable indicator for visceral adiposity. However, it remains largely unexplored on its association with fasting plasma glucose (FPG). The current study aims to explore the association between VAI and FPG using a representative dataset. Methods A cross-sectional study was carried out based on the dataset from National Health and Nutrition Examination Survey (NHANES) 2017–2020. Univariate and Multiple linear regression analysis were performed to explore the relationship between VAI and FPG. Generalized additive model (GAM) and smooth curve fitting analysis were performed to explore the nonlinear relationship between VAI and FPG. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of VAI for FPG elevation. Results A total of 4437 participants with complete data were finally included in the research. Individuals were divided into 4 quartiles according to the calculated VAI value: Q1 (VAI

Abstract Background Tissue culture is an effective method for the rapid breeding of seedlings and improving production efficiency, but explant browning is a key limiting factor of walnut tissue culture. Specifically, the polymerization of PPO-derived quinones that cause explant browning of walnut is not well understood. This study investigated explants of ‘Zanmei’ walnut shoot apices cultured in agar (A) or vermiculite (V) media, and the survival percentage, changes in phenolic content, POD and PPO activity, and JrPPO expression in explants were studied to determine the role of PPO in the browning of walnut explants. Results The results showed that the V media greatly reduced the death rate of explants, and 89.9 and 38.7% of the explants cultured in V media and A media survived, respectively. Compared with that of explants at 0 h, the PPO of explants cultured in A was highly active throughout the culture, but activity in those cultured in V remained low. The phenolic level of explants cultured in A increased significantly at 72 h but subsequently declined, and the content in the explants cultured in V increased to a high level only at 144 h. The POD in explants cultured in V showed high activity that did not cause browning. Gene expression assays showed that the expression of JrPPO1 was downregulated in explants cultured in both A and V. However, the expression of JrPPO2 was upregulated in explants cultured in A throughout the culture and upregulated in V at 144 h. JrPPO expression analyses in different tissues showed that JrPPO1 was highly expressed in stems, young leaves, mature leaves, catkins, pistils, and hulls, and JrPPO2 was highly expressed in mature leaves and pistils. Moreover, browning assays showed that both explants in A and leaf tissue exhibited high JrPPO2 activity. Conclusion The rapid increase in phenolic content caused the browning and death of explants. V media delayed the rapid accumulation of phenolic compounds in walnut explants in the short term, which significantly decreased explants mortality. The results suggest that JrPPO2 plays a key role in the oxidation of phenols in explants after branch injury.

Abstract Background Runs of homozygosity (ROH) are continuous homozygous regions typically located in the DNA sequence of diploid organisms. Identifications of ROH that lead to reduced performance can provide valuable insight into the genetic architecture of complex traits. Here, we systematically investigated the population genetic structure of five Anhui indigenous pig breeds (AHIPs), and compared them to those of five Western commercial pig breeds (WECPs). Furthermore, we examined the occurrence and distribution of ROHs in the five AHIPs and estimated the inbreeding coefficients based on the ROHs (FROH) and homozygosity (FHOM). Finally, we identified genomic regions with high frequencies of ROHs and annotated candidate genes contained therein. Results The WECPs and AHIPs were clearly differentiated into two separate clades consistent with their geographical origins, as revealed by the population structure and principal component analysis. We identified 13,530 ROHs across all individuals, of which 4,555 and 8,975 ROHs were unique to AHIPs and WECPs, respectively. Most ROHs identified in our study were short (

Abstract Background The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. Results In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. Conclusions The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.