Metabolic Networks Research Articles

Mapping transcriptomic profiles of seminal vesiculitis and experimental verification

ObjectiveSeminal vesiculitis is a prevalent infectious disease in the genitourinary system, but its intrinsic pathogenesis and molecular features remain unclear. MethodsIn this study, we utilized next-generation sequencing technology to demonstrate the transcriptomic landscapes of clinical specimens from seminal vesiculitis patients. We identified enriched Go Ontology terms, pathway maps, processes, toxicity, and metabolic networks. ResultsGlutathione transferase activity is highly enriched, indicating its essential role in the disease's development. Furthermore, we established inflammation models of the seminal vesicle and validated the crucial function of glutathione transferase activity in these models, ConclusionOur results provide a robust platform for further intensive studies of seminal vesiculitis.

Integration of network pharmacology, metabolomics and lipidomics for clarifying the role of sphingolipid metabolism in the treatment of liver cancer by regorafenib

AimsRegorafenib, an FDA-approved drug for advanced primary liver cancer (PLC), could provide survival benefits for patients. However, markers for its therapeutic sensitivity are lacking. This study seeks to identify sensitive targets of regorafenib in PLC from the perspective of small molecular metabolites. Materials and methodsInitiated with network pharmacology (NP) to map regorafenib's target landscape and metabolic regulatory network in liver cancer. Subsequently, regorafenib's impact on hepatoma cells was evaluated by flow cytometry, western blotting (WB) and cell viability assay. Advanced metabolomics and lipidomics were employed to elucidate regorafenib's metabolic reprogramming effects in liver cancer. Metabolic enzyme expression was assessed by WB, immunohistochemical and immunofluorescence assays. Ultimately, mendelian randomization (MR) analysis was utilized to investigate the potential causality of sphingolipid metabolism in hepatic cancer. Key findingsRegorafenib was observed to inhibit hepatoma cell proliferation and cell cycle progression at G0/G1 phase, resulting in significant alterations in sphingolipid levels. It promoted the significant accumulation of 16:0 dihydroceramide (16:0 dhCer) by upregulating ceramide synthase 6 (CERS6) expression and inhibiting dihydroceramide desaturase 1 (DEGS1) activity. The MR analysis revealed that DEGS1 was a risk factor for the development and progression of liver cancer, while cumulative 16:0 dhCer was a protective factor. SignificanceSphingolipids, particularly dhCer and regulatory enzymes, may be potential sensitive markers of regorafenib in the treatment of liver cancer, providing new insights for enhancing the treated efficacy of regorafenib in liver cancer.

AI-based automated construction of high-precision Geobacillus thermoglucosidasius enzyme constraint model

Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-generation cell factories. However, as a non-model microorganism, a lack of metabolic information significantly hampers the construction of high-precision metabolic flux models. Here, we propose a BioIntelliModel (BIM) strategy based on artificial intelligence technology for the automated construction of enzyme-constrained models. 1) . BIM utilises the Contrastive Learning Enabled Enzyme Annotation (CLEAN) prediction tool to analyse the entire genome sequence of G. thermoglucosidasius NCIMB 11955, uncovering potential functional proteins in non-model strains. 2). The MetaPatchM module of BIM automates the repair of the metabolic network model. 3). The Tianjin University of Science and Technology-kcat (TUST-kcat) module predicts the kcat values of enzymes within the model. 4). The Enzyme-insert procedure constructs an enzyme-constrained model and performs a global scan to address overconstraint issues. Enzymatic data were automatically integrated into the metabolic flux model, creating an enzyme-constrained model, ec_G-ther11955. To validate model accuracy, we used both the p-thermo and ec_G-ther11955 models to predict riboflavin production strategies. The ec_G-ther11955 model demonstrated significantly higher accuracy. To further verify its efficacy, we employed ec_G-ther11955 to guide the rational design of L-valine-producing strains. Using the Optimisation Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (OptForce), Predictive Knockout Targeting (PKT), and Flux Scanning based on Enforced Objective Flux (FSEOF) algorithms, we identified 24 knockout and overexpression targets, achieving an accuracy rate of 87.5%. Ultimately, this led to an increase of 664.04% in L-valine titre. This study provides a novel strategy for rapidly constructing non-model strain models and demonstrates the tremendous potential of artificial intelligence in metabolic engineering.

Genome-Scale Metabolic Modeling of Halomonas elongata 153B Explains Polyhydroxyalkanoate and Ectoine Biosynthesis in Hypersaline Environments.

Halomonas elongata thrives in hypersaline environments producing polyhydroxyalkanoates (PHAs) and osmoprotectants such as ectoine. Despite its biotechnological importance, several aspects of the dynamics of its metabolism remain elusive. Here, we construct and validate a genome-scale metabolic network model for H. elongata 153B. Then, we investigate the flux distribution dynamics during optimal growth, ectoine, and PHA biosynthesis using statistical methods, and a pipeline based on shadow prices. Lastly, we use optimization algorithms to uncover novel engineering targets to increase PHA production. The resulting model (iEB1239) includes 1534 metabolites, 2314 reactions, and 1239 genes. iEB1239 can reproduce growth on several carbon sources and predict growth on previously unreported ones. It also reproduces biochemical phenotypes related to Oad and Ppc gene functions in ectoine biosynthesis. A flux distribution analysis during optimal ectoine and PHA biosynthesis shows decreased energy production through oxidative phosphorylation. Furthermore, our analysis unveils a diverse spectrum of metabolic alterations that extend beyond mere flux changes to encompass heightened precursor production for ectoine and PHA synthesis. Crucially, these findings capture other metabolic changes linked to adaptation in hypersaline environments. Bottlenecks in the glycolysis and fatty acid metabolism pathways are identified, in addition to PhaC, which has been shown to increase PHA production when overexpressed. Overall, our pipeline demonstrates the potential of genome-scale metabolic models in combination with statistical approaches to obtain insights into the metabolism of H. elongata. Our platform can be exploited for researching environmental adaptation, and for designing and optimizing metabolic engineering strategies for bioproduct synthesis.

Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli

Engineered microorganisms have emerged as viable alternatives for limonene production. However, issues such as low enzyme abundance or activities, and regulatory feedback/forward inhibition may reduce yields. To understand the underlying metabolism, we adopted a systems biology approach for an engineered limonene-producing Escherichia coli strain K-12 MG1655. Firstly, we generated time-series metabolomics data and, secondly, developed a dynamic model based on enzyme dynamics to track the native metabolic networks and the engineered mevalonate pathway. After several iterations of model fitting with experimental profiles, which also included 13C-tracer studies, we performed in silico knockouts (KOs) of all enzymes to identify bottleneck(s) for optimal limonene yields. The simulations indicated that ALDH/ADH (aldehyde dehydrogenase/alcohol dehydrogenase) and LDH (lactate dehydrogenase) suppression, and HK (hexokinase) enhancement would increase limonene yields. Experimental confirmation was achieved, where ALDH-ADH and LDH KOs, and HK overexpression improved limonene yield by 8- to 11-fold. Our systems biology approach can guide microbial strain re-engineering for optimal target production.

Algorithms for evaluation of minimal cut sets

Objective:We propose a way to enhance the evaluation of minimal cut sets (MCSs) in biological systems modeled by Petri nets, by providing criteria and methodology for determining their optimality in disabling specific processes without affecting critical system components. Methods:This study concerns Petri nets to model biological systems and utilizes two primary approaches for MCS evaluation. First is the analyzing impact on t-invariants to identify structural dependencies. Second is assessing the impact on potentially starved transitions caused by the inactivity of specific MCSs. This approach deal with net dynamics. These methodologies aim to offer practical tools for assessing the quality and effectiveness of MCSs. Results:The proposed methodologies were applied to two case studies. In the first case, a cholesterol metabolism network was analyzed to investigate how local inflammation and oxidative stress, in conjunction with cholesterol imbalances, influence the progression of atherosclerosis. The MCSs were ranked, with the top sets presented, focusing on those that disabled the fewest number of t-invariants. In the second case, a carbohydrate metabolism disorder model was examined to understand its impact on atherosclerosis progression. The analysis aimed to identify MCSs that could inhibit the atherosclerosis process by targeting specific transitions. Both studies utilized the Holmes software for calculations, demonstrating the effectiveness of the proposed evaluation methodologies in ranking MCSs for practical biological applications. Conclusion:The algorithms proposed in this paper offer an analytical approach for evaluating the quality of MCSs in biological systems. By providing criteria for MCS optimality, these approaches have potential to enhance the utility of MCS analysis in systems biology, aiding in the understanding and manipulation of complex biological networks.Algorithm are implemented within Holmes software, an open-source project available at https://github.com/bszawulak/HolmesPN.

Glucosinolate structural diversity shapes recruitment of a metabolic network of leaf-associated bacteria

Host defenses can have broader ecological roles, but how they shape natural microbiome recruitment is poorly understood. Aliphatic glucosinolates (GLSs) are secondary defense metabolites in Brassicaceae plant leaves. Their genetically defined structure shapes interactions with pests in Arabidopsis thaliana leaves, and here we find that it also shapes bacterial recruitment. In model genotype Col-0, GLSs (mostly 4-methylsulfinylbutyl-GLS) have no clear effect on natural leaf bacterial recruitment. In a genotype from a wild population, however, GLSs (mostly allyl-GLS) enrich specific taxa, mostly Comamonadaceae and Oxalobacteraceae. Consistently, Comamonadaceae are also enriched in wild A. thaliana, and Oxalobacteraceae are enriched from wild plants on allyl-GLS as carbon source, but not on 4-methylsulfinylbutyl-GLS. Recruitment differences between GLS structures most likely arise from bacterial myrosinase specificity. Community recruitment is then defined by metabolic cross-feeding among bacteria. The link of genetically defined metabolites to recruitment could lead to new strategies to shape plant microbiome balance.

Understanding metabolic diversification in plants: branchpoints in the evolution of specialized metabolism.

Plants are chemical engineers par excellence. Collectively they make a vast array of structurally diverse specialized metabolites. The raw materials for building new pathways (genes encoding biosynthetic enzymes) are commonly recruited directly or indirectly from primary metabolism. Little is known about how new metabolic pathways and networks evolve in plants, or what key nodes contribute to branches that lead to the biosynthesis of diverse chemicals. Here we review the molecular mechanisms underlying the generation of biosynthetic branchpoints. We also consider examples in which new metabolites are formed through the joining of precursor molecules arising from different biosynthetic routes, a scenario that greatly increases both the diversity and complexity of specialized metabolism. Given the emerging importance of metabolic gene clustering in helping to identify new enzymes and pathways, we further cover the significance of biosynthetic gene clusters in relation to metabolic networks and dedicated biosynthetic pathways. In conclusion, an improved understanding of the branchpoints between metabolic pathways will be key in order to be able to predict and illustrate the complex structure of metabolic networks and to better understand the plasticity of plant metabolism. This article is part of the theme issue 'The evolution of plant metabolism'.

ITraNet: A Web-Based Platform for integrated Trans-Omics Network Visualization and Analysis

Abstract Motivation Visualization and analysis of biological networks play crucial roles in understanding living systems. Biological networks include diverse types, from gene regulatory networks and protein–protein interactions to metabolic networks. Metabolic networks include substrates, products, and enzymes, which are regulated by allosteric mechanisms and gene expression. However, the analysis of these diverse omics types is challenging due to the diversity of databases and the complexity of network analysis. Results We developed iTraNet, a web application that visualizes and analyzes trans-omics networks involving four types of networks: gene regulatory networks; protein–protein interactions; metabolic networks; and metabolite exchange networks. Using iTraNet, we found that in wild-type mice, hub molecules within the network tended to respond to glucose administration, whereas in ob/ob mice, this tendency disappeared. With its ability to facilitate network analysis, we anticipate that iTraNet will help researchers gain insights into living systems. Availability iTraNet is available at https://itranet.streamlit.app/. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Applications of ancestral sequence reconstruction for understanding the evolution of plant specialized metabolism.

Studies of enzymes in modern-day plants have documented the diversity of metabolic activities retained by species today but only provide limited insight into how those properties evolved. Ancestral sequence reconstruction (ASR) is an approach that provides statistical estimates of ancient plant enzyme sequences which can then be resurrected to test hypotheses about the evolution of catalytic activities and pathway assembly. Here, I review the insights that have been obtained using ASR to study plant metabolism and highlight important methodological aspects. Overall, studies of resurrected plant enzymes show that (i) exaptation is widespread such that even low or undetectable levels of ancestral activity with a substrate can later become the apparent primary activity of descendant enzymes, (ii) intramolecular epistasis may or may not limit evolutionary paths towards catalytic or substrate preference switches, and (iii) ancient pathway flux often differs from modern-day metabolic networks. These and other insights gained from ASR would not have been possible using only modern-day sequences. Future ASR studies characterizing entire ancestral metabolic networks as well as those that link ancient structures with enzymatic properties should continue to provide novel insights into how the chemical diversity of plants evolved. This article is part of the theme issue 'The evolution of plant metabolism'.

Biotic interactions, evolutionary forces and the pan-plant specialized metabolism.

Plant specialized metabolism has a complex evolutionary history. Some aspects are conserved across the green lineage, but many metabolites are unique to certain lineages. The network of specialized metabolism continuously diversified, simplified or reshaped during the evolution of streptophytes. Many routes of pan-plant specialized metabolism are involved in plant defence. Biotic interactions are recalled as major drivers of lineage-specific metabolomic diversification. However, the consequences of this diversity of specialized metabolism in the context of plant terrestrialization and land plant diversification into the major lineages of bryophytes, lycophytes, ferns, gymnosperms and angiosperms remain only little explored. Overall, this hampers conclusions on the evolutionary scenarios that shaped specialized metabolism. Recent efforts have brought forth new streptophyte model systems, an increase in genetically accessible species from distinct major plant lineages, and new functional data from a diversity of land plants on specialized metabolic pathways. In this review, we will integrate the recent data on the evolution of the plant immune system with the molecular data of specialized metabolism and its recognition. Based on this we will provide a contextual framework of the pan-plant specialized metabolism, the evolutionary aspects that shape it and the impact on adaptation to the terrestrial environment.This article is part of the theme issue 'The evolution of plant metabolism'.

Evolution of plant metabolism: the state-of-the-art.

Immense chemical diversity is one of the hallmark features of plants. This chemo-diversity is mainly underpinned by a highly complex and biodiverse biochemical machinery. Plant metabolic enzymes originated and were inherited from their eukaryotic and prokaryotic ancestors and further diversified by the unprecedentedly high rates of gene duplication and functionalization experienced in land plants. Unlike prokaryotic microbes, which display frequent horizontal gene transfer events and multiple inputs of energy and organic carbon, land plants predominantly rely on organic carbon generated from CO2 and have experienced relatively few gene transfers during their recent evolutionary history. As such, plant metabolic networks have evolved in a stepwise manner using existing networks as a starting point and under various evolutionary constraints. That said, until recently, the evolution of only a handful of metabolic traits had been extensively investigated and as such, the evolution of metabolism has received a fraction of the attention of, the evolution of development, for example. Advances in metabolomics and next-generation sequencing have, however, recently led to a deeper understanding of how a wide range of plant primary and specialized (secondary) metabolic pathways have evolved both as a consequence of natural selection and of domestication and crop improvement processes. This article is part of the theme issue 'The evolution of plant metabolism'.

The Use of Patient-Derived Organoids in the Study of Molecular Metabolic Adaptation in Breast Cancer

Around 13% of women will likely develop breast cancer during their lifetime. Advances in cancer metabolism research have identified a range of metabolic reprogramming events, such as altered glucose and amino acid uptake, increased reliance on glycolysis, and interactions with the tumor microenvironment (TME), all of which present new opportunities for targeted therapies. However, studying these metabolic networks is challenging in traditional 2D cell cultures, which often fail to replicate the three-dimensional architecture and dynamic interactions of real tumors. To address this, organoid models have emerged as powerful tools. Tumor organoids are 3D cultures, often derived from patient tissue, that more accurately mimic the structural and functional properties of actual tumor tissues in vivo, offering a more realistic model for investigating cancer metabolism. This review explores the unique metabolic adaptations of breast cancer and discusses how organoid models can provide deeper insights into these processes. We evaluate the most advanced tools for studying cancer metabolism in three-dimensional culture models, including optical metabolic imaging (OMI), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and recent advances in conventional techniques applied to 3D cultures. Finally, we explore the progress made in identifying and targeting potential therapeutic targets in breast cancer metabolism.

Integrated bioinformatics analysis identifies PCSK9 as a prognosticator correlated with lipid metabolism in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is the most frequent kind of pancreatic cancer (PC). Recent studies suggest that lipid metabolism facilitates tumorigenesis, disease progression, and resistance to therapy by promoting lipid synthesis, accumulation, and breakdown. Thus, exploring the lipid metabolism network could unveil novel therapeutic avenues for early detection, precision medicine, and prognostication in PAAD. This project intends to develop new lipid metabolism-related biomarkers for PAAD diagnosis and investigate the link between important genes and immune cell infiltration (ICI).MethodsTissue samples from 20 PAAD patients and 20 healthy controls were obtained. Analysis were focused on the datasets GSE71729 and GSE16515, which include samples of PAAD (n = 161) and those from healthy human tissue (n = 61), derived from the GEO database. Knockdown of PCSK9 on PC cells were conducted by si-RNA and sh-RNA. Migration and cell functional experiments were performed to assess the role of PCSK9 in cell multiplication. Furthermore, a xenograft mouse model was employed to confirm PCSK9's function in vivo.ResultsThe expression level of Proprotein convertase subtilisin/kexin type 9 (PCSK9) is significantly elevated in tissues affected by PAAD when compared to normal tissues. Survival analyses indicated that increased PCSK9 levels are inversely related to overall and disease-free survival (DFS). PCSK9’s functional annotation associated it with the cell cycle and metabolism, especially energy metabolism. Examination of ICI data determined that PCSK9 expression demonstrated an unambiguous association with the M0 macrophages, T follicular helper cells (Tfh), gamma delta T cells and activated DC, and an inverse relationship with Monocytes, CD8+ T cells, memory B cells, resting CD4+ memory T cells, activated NK cells and resting DC abundance. PCSK9 expression knockdown has the ability to impede PC cells’ migration and proliferation.ConclusionOur study identified PCSK9 as a critical gene in PAAD. Expression levels of PCSK9 varied between PAAD and normal samples. ROC analysis verified PCSK9's strong capacity to differentiate PC from normal samples. Importantly, PCSK9 expression was considerably elevated in PC cell lines and tissues. Furthermore, PCSK9 stimulates the migration and proliferation of tumor cells in vivo and vitro.

Roles of network topology in the relaxation dynamics of simple chemical reaction network models

Understanding the relationship between the structure of chemical reaction networks and their reaction dynamics is essential for unveiling the design principles of living organisms. However, while some network-structural features are known to relate to the steady-state characteristics of chemical reaction networks, mathematical frameworks describing the links between out-of-steady-state dynamics and network structure are still underdeveloped. Here, we characterize the out-of-steady-state behavior of a class of artificial chemical reaction networks consisting of the ligation and splitting reactions of polymers. Within this class, we examine minimal networks that can convert a given set of sources (e.g., nutrients) to a specified set of targets (e.g., biomass precursors). By exploring the dynamics of the models with a simple setup, we find three distinct types of relaxation dynamics after perturbation from a steady-state: exponential-, power-law-, and plateau-dominated. We computationally show that we can predict this out-of-steady-state dynamical behavior from just three features computed from the network’s stoichiometric matrix, namely, (1) the rank gap, determining the existence of a steady-state; (2) the left null-space, being related to conserved quantities in the dynamics; and (3) the stoichiometric cone, dictating the range of achievable chemical concentrations. We further demonstrate that these three quantities relates to the type of relaxation dynamics of combinations of our minimal networks, larger networks with many redundant pathways, and a real example of a metabolic network. The relationship between the topological features of reaction networks and the relaxation dynamics presented here are useful clues for understanding the design of metabolic reaction networks as well as industrially useful chemical production pathways.

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.

A hierarchy of metabolite exchanges in metabolic models of microbial species and communities.

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells' metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.

Toxicity mechanism of organosilicon adjuvant in combination with S-metolachlor on Vigna angularis

The widespread use of S-metolachlor (ME) in agriculture to suppress weeds and boost crop yields, particularly in cultivating Vigna angularis, is well established. However, the application of organosilicon adjuvants with herbicides has potential threats to non-target crops. This study investigates the toxicity symptoms and mechanisms when V. angularis is exposed to ME in conjunction with a common organosilicon adjuvant. Results indicate that ME inhibits the growth of V. angularis seedlings, and adding adjuvants could aggravate the negative effects of ME. According to the growth index of seedlings, the adjuvant increased the toxicity of ME by 84–96 %. Additionally, the chlorophyll content, root permeability, and antioxidant indicators in the seedlings were also adversely affected. Integrated metabolomics and transcriptomics analyses reveal that differentially abundant metabolites (DAMs) and differentially expressed genes (DEGs) are mainly enriched in four ways: "lysine degradation," "ABC transporters," "phenylalanine metabolism," and "monoterpenoid biosynthesis." The metabolic pathways and gene regulatory network involving 11 DAMs and 22 DEGs are associated with the physiological processes affected by ME and the adjuvant. This study provides guidance for the application of herbicides and their adjuvants in agricultural production to minimize adverse effects on non-target crops.

A candidate loss-of-function variant in SGIP1 causes synaptic dysfunction and recessive parkinsonism

Synaptic dysfunction is recognized as an early step in the pathophysiology of parkinsonism. Several genetic mutations affecting the integrity of synaptic proteins cause or increase the risk of developing disease. We have identified a candidate causative mutation in synaptic "SH3GL2 Interacting Protein 1" (SGIP1), linked to early-onset parkinsonism in a consanguineous Arab family. Additionally, affected siblings display intellectual, cognitive, and behavioral dysfunction. Metabolic network analysis of [18F]-fluorodeoxyglucose positron emission tomography scans shows patterns very similar to those of idiopathic Parkinson's disease. We show that the identified SGIP1 mutation causes a loss of protein function, and analyses in newly created Drosophila models reveal movement defects, synaptic transmission dysfunction, and neurodegeneration, including dopaminergic synapse loss. Histology and correlative light and electron microscopy reveal the absence of synaptic multivesicular bodies and the accumulation of degradative organelles. This research delineates a putative form of recessive parkinsonism, converging on defective synaptic proteostasis and opening avenues for diagnosis, genetic counseling, and treatment.

The deubiquitinase Ubp3/Usp10 constrains glucose-mediated mitochondrial repression via phosphate budgeting.

Many cells in high glucose repress mitochondrial respiration, as observed in the Crabtree and Warburg effects. Our understanding of biochemical constraints for mitochondrial activation is limited. Using a Saccharomyces cerevisiae screen, we identified the conserved deubiquitinase Ubp3 (Usp10), as necessary for mitochondrial repression. Ubp3 mutants have increased mitochondrial activity despite abundant glucose, along with decreased glycolytic enzymes, and a rewired glucose metabolic network with increased trehalose production. Utilizing ∆ubp3 cells, along with orthogonal approaches, we establish that the high glycolytic flux in glucose continuously consumes free Pi. This restricts mitochondrial access to inorganic phosphate (Pi), and prevents mitochondrial activation. Contrastingly, rewired glucose metabolism with enhanced trehalose production and reduced GAPDH (as in ∆ubp3 cells) restores Pi. This collectively results in increased mitochondrial Pi and derepression, while restricting mitochondrial Pi transport prevents activation. We therefore suggest that glycolytic flux-dependent intracellular Pi budgeting is a key constraint for mitochondrial repression.