A novel pyruvate kinase and its application in lactic acid production under oxygen deprivation in Corynebacterium glutamicum

BACKGROUND: Pyruvate kinase (Pyk) catalyzes the generation of pyruvate and ATP in glycolysis and functions as a key switch in the regulation of carbon flux distribution. Both the substrates and products of Pyk are involved in the tricarboxylic acid cycle, anaplerosis and energy anabolism, which places Pyk at a primary metabolic intersection. Pyks are highly conserved in most bacteria and lower eukaryotes. Corynebacterium glutamicum is an industrial workhorse for the production of various amino acids and organic acids. Although C. glutamicum was assumed to possess only one Pyk (pyk1, NCgl2008), NCgl2809 was annotated as a pyruvate kinase with an unknown role. RESULTS: Here, we identified that NCgl2809 was a novel pyruvate kinase (pyk2) in C. glutamicum. Complementation of the WTΔpyk1Δpyk2 strain with the pyk2 gene restored its growth on d-ribose, which demonstrated that Pyk2 could substitute for Pyk1 in vivo. Pyk2 was co-dependent on Mn(2+) and K(+) and had a higher affinity for ADP than phosphoenolpyruvate (PEP). The catalytic activity of Pyk2 was allosterically regulated by fructose 1,6-bisphosphate (FBP) activation and ATP inhibition. Furthermore, pyk2 and ldhA, which encodes l-lactate dehydrogenase, were co-transcribed as a bicistronic mRNA under aerobic conditions and pyk2 deficiency had a slight effect on the intracellular activity of Pyk. However, the mRNA level of pyk2 in the wild-type strain under oxygen deprivation was 14.24-fold higher than that under aerobic conditions. Under oxygen deprivation, pyk1 or pyk2 deficiency decreased the generation of lactic acid, and the overexpression of either pyk1 or pyk2 increased the production of lactic acid as the activity of Pyk increased. Fed-batch fermentation of the pyk2-overexpressing WTΔpyk1 strain produced 60.27 ± 1.40 g/L of lactic acid, which was a 47% increase compared to the parent strain under oxygen deprivation. CONCLUSIONS: Pyk2 functioned as a pyruvate kinase and contributed to the increased level of Pyk activity under oxygen deprivation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12896-016-0313-6) contains supplementary material, which is available to authorized users

One-shot Neural Backdoor Erasing via Adversarial Weight Masking

Recent studies show that despite achieving high accuracy on a number of real-world applications, deep neural networks (DNNs) can be backdoored: by injecting triggered data samples into the training dataset, the adversary can mislead the trained model into classifying any test data to the target class as long as the trigger pattern is presented. To nullify such backdoor threats, various methods have been proposed. Particularly, a line of research aims to purify the potentially compromised model. However, one major limitation of this line of work is the requirement to access sufficient original training data: the purifying performance is a lot worse when the available training data is limited. In this work, we propose Adversarial Weight Masking (AWM), a novel method capable of erasing the neural backdoors even in the one-shot setting. The key idea behind our method is to formulate this into a min-max optimization problem: first, adversarially recover the trigger patterns and then (soft) mask the network weights that are sensitive to the recovered patterns. Comprehensive evaluations of several benchmark datasets suggest that AWM can largely improve the purifying effects over other state-of-the-art methods on various available training dataset sizes.Comment: Accepted by NeurIPS 2022 (19 pages, 6 figures, 10 tables

Case Control Association Study of WNT Signaling Pathway-related Genes and Osteoporosis Risk Among Chinese Postmenopausal Women

Abstract Background: The WNT signaling pathway is involved in the regulation of bone homeostasis, and the effects of WNT signaling pathway related genes (WLS, WNT16 and LRP5) on osteoporosis risk among Chinese postmenopausal women is still unknown. Hence, we performed a case-control study to assess the association of WNT signaling pathway related genes and osteoporosis risk.Methods: This study involved 1026 women (515 osteoporosis patients and 511 controls) of postmenopausal age who were randomly sampled from Xi'an 630 Hospital, Shaanxi Province, China. Eleven genetic polymorphisms in WLS (rs2566755, rs12407028, rs2566752 and rs7554551), WNT16 (rs3779381, rs3801387, rs917727 and rs7776725) and LRP5 (rs2291467, rs11228240 and rs12272917) were selected and genotyped using the Agena MassARRAY iPLEX system. The association of the genetic polymorphisms and osteoporosis risk was assessed by odds ratios and 95% confidence intervals. The Multifactor Dimensionality Reduction (MDR) method was conducted to analyze SNP-SNP interaction.Results: We found that LRP5 polymorphisms (rs2291467, rs11228240 and rs12272917) were significantly associated with a decreased risk of osteoporosis in homozygotes, both in recessive and additive models (P &lt; 0.05). Stratification analysis showed that LRP5 polymorphisms (rs2291467, rs11228240 and rs12272917) significantly decreased the osteoporosis risk in the subgroup of BMI ≤ 24 (P &lt; 0.05) and that individuals carrying a heterozygote genotype of WNT16 polymorphisms (rs3779381, rs3801387, rs917727 and rs7776725) had a higher osteoporosis risk in the subgroup of BMI &gt; 24 (P &lt; 0.05). We observed that three blocks (block 1: rs2566755 and rs12407028, block 2: rs3779381, rs3801387, rs917727 and rs7776725, block 3: rs2291467 and rs11228240), Trs2291467Trs11228240 and Crs2291467Crs11228240 had a strong association with a lower risk of osteoporosis. Additionally, MDR analysis revealed that a four-locus model (rs2566752 and rs2566755 in WLS, rs7776725 in WNT16, rs12272917 in LRP5) was significantly associated with osteoporosis risk. Conclusions: Our findings suggested that WLS, WNT16 and LRP5 genetic polymorphisms were associated with osteoporosis risk among Chinese postmenopausal women.</jats:p

Diversity and Distribution of Methane Functional Microorganisms in Sedimentary Columns of Hongfeng Reservoir in Different Seasons

Freshwater ecosystem is a significant natural source of CH4 emission in the atmosphere. To fully understand the dynamics of methane emissions in reservoirs, it is essential to grasp the temporal and vertical distribution patterns, as well as the factors that influence the methanogenic bacterial communities within the sediments. This study investigates the methane dynamics, carbon isotope fractionation (δ13CH4), and abundance of functional microorganisms along the geochemical gradient in the in situ sedimentary column of Hongfeng Reservoir (China). Notably, the methane concentration in sediment in summer ranged in 15.39–127.22 µmol/L, which is twice as high as wintertime concentrations in the surface layer near the sediment–water interface (0–10 cm depth). Illumina sequencing of the sediments identified 11 genera affiliated with methanogenic archaea, with dominant genus Methanosaeta reaching a relative abundance of 34.95% in summer. The total carbon (TOC) content in sedimentary columns in different seasons is positively correlated with Methanosarcina (P < 0.05). In addition, seasonal discrepancies are observed in the sediment profiles for total nitrogen (TN), sulfate (SO42−), and ferrous iron (Fe2+) concentrations. The concentration of total nitrogen (TN) is higher in summer than in winter. In summer, sulfate accumulates in the middle layer of the sedimentary column, while in winter, the maximum concentration of sulfate in the surface layer reaches 0.65 mmol/L. These geochemical gradients drive the biological transformation of nitrogen, sulfur, and iron, may also be linked to the consumption of methane. Thus, it is established that the temporal and spatial dynamics of methanogenic communities in sediments significantly influence the fluctuations in methane release fluxes within reservoirs, highlighting the necessity to account for seasonal biological variations when assessing greenhouse gas emissions from reservoirs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024

A new genome-scale metabolic model of Corynebacterium glutamicum and its application

Abstract Background Corynebacterium glutamicum is an important platform organism for industrial biotechnology to produce amino acids, organic acids, bioplastic monomers, and biofuels. The metabolic flexibility, broad substrate spectrum, and fermentative robustness of C. glutamicum make this organism an ideal cell factory to manufacture desired products. With increases in gene function, transport system, and metabolic profile information under certain conditions, developing a comprehensive genome-scale metabolic model (GEM) of C. glutamicum ATCC13032 is desired to improve prediction accuracy, elucidate cellular metabolism, and guide metabolic engineering. Results Here, we constructed a new GEM for ATCC13032, iCW773, consisting of 773 genes, 950 metabolites, and 1207 reactions. Compared to the previous model, iCW773 supplemented 496 gene–protein-reaction associations, refined five lumped reactions, balanced the mass and charge, and constrained the directionality of reactions. The simulated growth rates of C. glutamicum cultivated on seven different carbon sources using iCW773 were consistent with experimental values. Pearson’s correlation coefficient between the iCW773-simulated and experimental fluxes was 0.99, suggesting that iCW773 provided an accurate intracellular flux distribution of the wild-type strain growing on glucose. Furthermore, genetic interventions for overproducing l-lysine, 1,2-propanediol and isobutanol simulated using OptForceMUST were in accordance with reported experimental results, indicating the practicability of iCW773 for the design of metabolic networks to overproduce desired products. In vivo genetic modifications of iCW773-predicted targets resulted in the de novo generation of an l-proline-overproducing strain. In fed-batch culture, the engineered C. glutamicum strain produced 66.43 g/L l-proline in 60 h with a yield of 0.26 g/g (l-proline/glucose) and a productivity of 1.11 g/L/h. To our knowledge, this is the highest titer and productivity reported for l-proline production using glucose as the carbon resource in a minimal medium. Conclusions Our developed iCW773 serves as a high-quality platform for model-guided strain design to produce industrial bioproducts of interest. This new GEM will be a successful multidisciplinary tool and will make valuable contributions to metabolic engineering in academia and industry