Biology subjects

Schmitz, J.

Publications and source records attributed to Schmitz, J..

At least 19 recordsLinked to original sources

bioRxiv2026

In which cases does it make sense to classify an individual into one population and what are the alternatives?

During biogeographical ancestry (BGA) analysis, an unknown individual for whom ancestry informative markers (AIMs) were analyzed is often assigned to one of a few reference populations. For some individuals, such an assignment is not meaningful: the two parents of the individual may come from different populations, or the individual may come from a population not included in the reference database. In this study, we examined classification methods in combination with 104 AIMs from the VISAGE Enhanced Tool to provide one of the following three possible outcomes: (i) in-reference (IR), with an assignment to one of the reference populations; (ii) admixed (AD), with parents from two in-reference populations; and (iii) out-of-reference (OR), where the individual's ancestry is not sufficiently represented by the reference populations. We therefore compared seven classifiers in combination with class-specific acceptance thresholds for the OR decision. The best-performing method according to our selection criterion was a naive Bayes classifier over pairs of populations, which we call the recent naive Bayes (RNB) classifier. Populations held out of the reference panel were correctly rejected in $84.9\%$ of cases. We further tested the method on $800$ individuals from seven additional groups not included in the reference panel. Only $29.4\%$ of individuals from four non-admixed populations were rejected, resembling joint ancestry with reference populations. In addition, we investigated the prediction accuracy in a newly collected dataset of 28 individuals with self-reported ancestry by analyzing AIMs using massively parallel sequencing.

genetics↗

bioRxiv2026

Cyst-type epithelial heterogeneity shapes therapeutic responsiveness in ADPKD

Autosomal dominant polycystic kidney disease (ADPKD) exhibits substantial interpatient variability in disease course and therapeutic response, but the cellular basis for this variability remains poorly understood. Here, we combine single-nucleus RNA sequencing of human cyst epithelia with machine learning-based histological analysis of >1,800 cysts to resolve three epithelial cyst types--proximal tubule-like, collecting duct-like, and mixed. These cyst types display distinct injury states, metabolic programs, and stromal microenvironments, including a mixed-cyst niche enriched for CCL2-associated inflammatory signaling. Expression of key therapeutic targets was highly cell-type specific with CFTR enriched in proximal-like epithelia, whereas AVPR2 expression was confined to AQP2-positive collecting duct-like cells. Cyst-type composition varied widely across patients and in an orthologous mouse model (Pkd1RC/RC) in which the burden of AQP2-positive cysts correlated with responsiveness to tolvaptan. These findings identify cyst-type heterogeneity as a major determinant of molecular pathway activation and predictability of therapeutic response in ADPKD.

molecular biology↗

bioRxiv2026

Integrated kidney and urine proteomics define encrypted antimicrobial peptides as effectors of host defence in human pyelonephritis

Antimicrobial peptides (AMPs) are key effectors of host defence, however, their functional deployment across renal tissue and urine in pyelonephritis (PN) remains incompletely understood. Here, we integrate kidney and urine proteomics with urinary peptidomics and computational prediction to define AMP organisation and function. Proteomic analysis indicated coordinated induction of multiple AMPs in infected kidneys. These patterns were recapitulated in the urinary proteome, where AMP abundance correlated with leukocyte counts. Multiplex immunofluorescence microscopy localised these AMPs to myeloid cells, identifying them as central effector sources. Importantly, analysis of the urine peptidome revealed multiple encrypted AMPs (EPs), which arise from proteolytic processing of precursor proteins. To systematically assess their relevance for host defence, we applied an ensemble of machine learning-based predictors to prioritise candidates with activity in the urinary environment. This approach identified several potential EPs, among which the S100A12-derived peptide Calcitermin was confirmed in patient urine. Furthermore, it exerts antibacterial activity against uropathogenic E. coli (UPEC) and modulates myeloid cell responses. Together, these findings define a coordinated and compartmentalised AMP defence programme in human PN that extends beyond increased peptide expression, highlighting EPs as functionally relevant effectors with therapeutic potential.

immunology↗

bioRxiv2026

Lactate-dependent metabolic rewiring is associated with CD163+ macrophage polarization in human pyelonephritis

Chronic pyelonephritis is characterized by persistent bacterial infection of the kidney and a dysregulated immune response that promotes disease progression. Macrophages are central regulators of antibacterial immunity in the urinary tract. However, the mechanisms underlying their functional reprogramming in chronic infection remain poorly understood. Here, we identify lactate as a key metabolic determinant of macrophage polarization in human pyelonephritis. Proteomic analysis of human kidney tissue revealed extensive metabolic remodelling, including upregulation of enzymes involved in glycolysis. Consistent with this, lactate levels were significantly elevated in urine and plasma of pyelonephritis patients. Concomitantly, we observed a pronounced accumulation of CD163 macrophages in infected kidneys, representing a distinct macrophage subset with immunomodulatory function. Correlation-based network analysis revealed a strong association between CD163 and lactate dehydrogenase A, supporting a functional association between lactate metabolism and macrophage polarization. Mechanistically, exposure of murine bone marrow-derived macrophages to lactate induced intracellular protein lactylation and promoted polarization toward a CD163 phenotype, defining a metabolically imprinted macrophage state distinct from classical activation paradigms. Proteomic profiling demonstrated extensive remodelling of macrophage protein expression in response to lactate with significant alteration of mediators of phagocytosis, Toll-like receptor signalling, and interferon response. Together, these findings identify lactate as a potent metabolic driver of macrophage reprogramming and establish a foundation for investigating lactylation-dependent immune dysfunction in chronic pyelonephritis.

immunology↗

bioRxiv2026

Comparing metabolic engineering scenarios using simulated design-build-test-learn-cycles

Design-Build-Test-Learn (DBTL) cycles are a widely employed engineering framework in metabolic engineering. Nonetheless, their performance depends on a wide range of experimental and algorithmic design choices, whose combined effects on the successful optimization of microbial strains remain an open question. In this study, we performed in-silico DBTL cycles based on metabolic kinetic models to quantitatively assess how key process parameters affect strain optimization outcomes across four distinct metabolic pathway models. This includes parameters governing DNA library design, experimental budget limitations, and machine learning configuration. The results show that screening capacity is a dominant driver of optimization success, whereas DNA sequencing capacity has surprisingly little impact, despite its importance for model training. Selecting top-producing strains for sequencing consistently outperforms stratified sampling, highlighting a trade-off between predictive accuracy and optimization efficiency. DNA library structure strongly affects performance: increasing the number of editable positions generally improves outcomes, while expanding the set of gene targets can hinder optimization due to increased dimensionality or sparse sampling. Together, these findings offer actionable guidance for designing more effective DBTL workflows and underscore the value of simulation frameworks for exploring metabolic engineering strategies prior to experimental implementation.

bioengineering↗

bioRxiv2025

Machine Learning-Assisted Pathway Optimization in Large Combinatorial Design Spaces: a p-Coumaric Acid Case Study

Combinatorial pathway optimization is an important tool for industrial metabolic engineering to improve titer, yield, or productivity of strains. Machine learning has been increasingly applied on many aspects of the Design-Build-Test-Learn (DBTL) cycle, an engineering framework that aims to navigate through the large landscape of theoretically possible designs using an iterative approach. While machine learning-assisted recommendation strategies have been successfully used to optimize strains, they have so far been limited to relatively small design spaces with few targeted elements. This small design space may limit key strengths of these approaches, such as strong predictive capabilities of supervised machine learning and exploration-exploitation schemes widely used in reinforcement learning and Bayesian optimization. In this work, two DBTL cycles are performed on Saccharomyces cerevisiae for p-coumaric acid production. We first perform a large library transformation on eighteen genes with twenty promoters, which expands the size of the combinatorial design space significantly (approximately 170 million configurations), followed by a smaller model-guided recommendation round. We use a machine learning-assisted recommendation strategy, based on the gradient bandit algorithm, parametrized to balance explo- ration and exploitation. We show that our recommendation strategy has a better performance than strain recommendation strategy using greedy strategies, such as feature importance-based methods. While balancing between exploration and exploitation has been shown to be impor- tant in many applications, we provide the first direct experimental illustration of this effect by recommending strains for scenarios with increasing exploitative-ness. A clear effect of the exploration-exploitation scenario on the p-coumaric acid production distribution of strains is observed, where a balanced scenario shows a higher variation in production over an exploratory or exploitative scenario. Interestingly, using an alternative top-producing parent strain with this balanced exploration-exploitation scheme gives the highest p-coumaric acid production, suggest- ing that model predictions outside of the training data distribution can still be used to perform successful strain recommendation. Overall, these results suggest that using machine learning- assisted strategies with balanced exploration-exploitation can be used to efficiently explore large combinatorial design spaces. The best engineered strain shows an increase in p-coumaric acid production of 137% over the parent strains and a 0.07g/g yield on glucose.

bioengineering↗

bioRxiv2025

A single pathogen-secreted protein reprograms plants for drought resilience

Climate change-enforced drought stress conditions and diseases caused by pathogens often co-occur and represent one of the greatest challenges in plant science1-3. Wilt pathogens that colonize water-conducting plant tissues can aggravate the problem and affect a wide range of agricultural crops4,5. However, whilst fungal infections with the vascular pathogen Verticillium dahliae are typically associated with wilt symptoms due to occlusion of xylem tissues, the related V. longisporum induces de novo formation of tracheary elements6,7. This promotes not only its virulence but also enables elevated water storage capacity of the infected host plant and resilience against drought stress conditions6,7. Here, we identified a secreted Verticillium protein, TRANSDIFFERENTIATION EFFECTOR (TRADE), which triggers cell identity switches of bundle sheath cells into tracheary elements. We show that TRADE interacts with the intracellular plant protein VARICOSE (VCS), a conserved component of the mRNA turnover machinery and ortholog of the metazoan protein ENHANCER OF DECAPPING 4 (EDC4/HEDLS/Ge-1)8. The TRADE-VCS interaction induces SUCROSE NON-FERMENTING 1 (SNF1)-related protein kinase (SRK)-dependent phosphorylation and thus dysfunction of VCS. This affects the abundance of mRNAs encoding master regulators of xylem differentiation and demonstrates how a single pathogen effector protein triggers complex tissue-specific developmental reprogramming and thus promotes abiotic stress resilience.

plant biology↗

bioRxiv2024

Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models

MotivationMetabolic kinetic models are widely used to model biological systems. Despite their widespread use, it remains challenging to parameterize these Ordinary Differential Equations (ODE) for large scale kinetic models. Recent work on neural ODEs has shown the potential for modeling time-series data using neural networks, and many methodological developments in this field can similarly be applied to kinetic models. ResultsWe have implemented a simulation and training framework for Systems Biology Markup Language (SBML) models using JAX/Diffrax, which we named jaxkineticmodel. JAX allows for automatic differentiation and just-in-time compilation capabilities to speed up the parameterization of kinetic models. We show the robust capabilities of training kinetic models using this framework on a large collection of SBML models with different degrees of prior information on parameter initialization. Finally, we showcase the training framework implementation on a complex model of glycolysis. These results show that our framework can be used to fit large metabolic kinetic models efficiently and provides a strong platform for modeling biological systems. ImplementationImplementation of jaxkineticmodel is available as a Python package at https://github.com/AbeelLab/jaxkineticmodel. Author summaryUnderstanding how metabolism works from a systems perspective is important for many biotechnological applications. Metabolic kinetic models help in achieving understanding, but there construction and parametrization has proven to be complex, especially for larger metabolic networks. Recent success in the field of neural ordinary differential equations in combination with other mathematical/computational techniques may help in tackling this issue for training kinetic models. We have implemented a Python package named jaxkineticmodel that can be used to build, simulate and train kinetic models, as well as compatibility with the Systems Biology Markup Language. This framework allows for efficient training of kinetic models on time-series concentration data using a neural ordinary differential equation inspired approach. We show the convergence properties on a large collection of SBML models, as well as experimental data. This shows a robust training process for models with hundreds of parameters, indicating that it can be used for large-scale kinetic model training.

systems biology↗

bioRxiv2024

Regulatory T cells crosstalk with tumor and endothelium through lymphotoxin signaling

Regulatory T cells (Tregs) are suppressors of anti-tumor immunity that exert multifaceted functions by signaling surrounding cells. We revealed Tregs use their high-level surface lymphotoxin (LT)1{beta}2 to preferentially stimulate LT{beta} receptor (LT{beta}R) nonclassical NF{kappa}B signaling on both tumor and lymphatic endothelial cells (LECs) to accelerate tumor growth and metastasis. Selectively targeting LT{beta}R nonclassical NF{kappa}B pathways on both tumors and LECs cocultured with Tregs, inhibited tumor growth and migration in vitro. Further, we identified protumorigenic chemokines and interferon-stimulated response genes selectively driven by LT{beta}R nonclassical NF{kappa}B in melanoma cells. Endothelial specific genes related to oncogenic process such as SOX18 and FLRT2 were identified to be driven under LT{beta}R nonclassical NF{kappa}B in LECs. Leveraging in vivo Treg LT1{beta}2 interactions with LT{beta}R on tumor and LECs, transfer of WT but not LT-deficient Tregs promoted transplanted WT B16F10 growth and tumor cell-derived CXCL1 and CXCL10 secretion in LT{beta}R-deficient host mice, and increased endothelial specific genes related to tumor angiogenesis and lymphangiogenesis, in WT mice bearing LT{beta}R-depleted melanoma. Selectively blocking LT{beta}R nonclassical NF{kappa}B pathways remarkably suppressed tumor growth and lymphatic metastasis by reducing tumor cell and LEC-derived CXCL1 and CXCL10 production, restricting Treg and myeloid-derived suppressor cell (MDSC) recruitment to tumor. It also retained intratumoral effector T cells, especially IFN{gamma}+ CD8 T cells by restraining Treg facilitated lymphatic vessel permeability. Our data revealed that Treg LT1{beta}2 promotes LT{beta}R nonclassical NF{kappa}B signaling in tumor cells and LECs providing a rational strategy to modulate Treg-mediated protumorigenic molecules to prevent tumor growth and metastasis.

cancer biology↗

bioRxiv2024

Clu1/Clu form mitochondria-associated granules upon metabolic transitions and regulate mitochondrial protein translation via ribosome interactions

Mitochondria perform essential metabolic functions and respond rapidly to changes in metabolic and stress conditions. As the majority of mitochondrial proteins are nuclear-encoded, intricate post-transcriptional regulation is crucial to enable mitochondria to adapt to changing cellular demands. The eukaryotic Clustered mitochondria protein family has emerged as an important regulator of mitochondrial function during metabolic shifts. Here, we show that the Drosophila melanogaster and Saccharomyces cerevisiae Clu/Clu1 proteins form dynamic, membraneless granules adjacent to mitochondria in response to metabolic changes. Yeast Clu1 regulates the translation of a subset of nuclear-encoded mitochondrial protein by interacting with their mRNAs while these are engaged in translation. We further show that Clu1 regulates translation by interacting with polysomes, independently of whether it is in a diffuse or granular state. Our results demonstrate remarkable functional conservation with other members of the Clustered mitochondria protein family and suggests that Clu/Clu1 granules isolate and concentrate ribosomes engaged in translating their mRNA targets, thus, integrating metabolic signals with the regulation of mitochondrial protein synthesis.

molecular biology↗

bioRxiv2024

CORONATINE INSENSITIVE 1-mediated repression of immunity-related genes in Arabidopsis roots is overcome upon infection with Verticillium longisporum

Verticillium longisporum is a soil-borne fungal pathogen causing vascular disease predominantly in Brassicaceae. We have reported previously that the receptor of the plant defense hormone jasmonoyl-isoleucine (JA-Ile), CORONATINE INSENSITIVE 1 (COI1), is required in roots for efficient proliferation of the fungus in the shoot implicating a mobile root-borne signal that influences the outcome of the disease in shoots. To explore the underlying mechanism we compared the root transcriptome of coi1 with the transcriptomes of three susceptible genotypes (wild-type, mutants deficient in JA-Ile and salicylic acid (SA) synthesis). At 10 days after infection, genes related to either xylem formation or plant immunity were induced independently of JA-Ile and SA. The biggest difference between the transcriptomes was due to 316 immunity-related genes that were pre-induced in coi1. Interfering with the expression of a subgroup of these genes partially suppressed the coi1 phenotype. We therefore hypothesize that mobile defense compounds secreted into the xylem and being transported with the transpiration stream confer tolerance to the shoot. We furthermore report that 149 of the COI1-repressed genes are induced in WT upon infection reaching similar levels as in mock-treated coi1. The majority of these were not further induced in coi1, indicating that COI1 is required for infection-induced expression.

molecular biology↗

bioRxiv2024

Viewing stomata in action: Autonomous in planta imaging of individual stomatal movement links morphology and kinetics

Stomata regulate plant gas exchange under changing environments, but observations of the dynamics of single stomata in planta are sparse. We developed a compact microscope system that can measure the kinetics of tens of stomata in planta simultaneously, with sub-minute time resolution. Dark field imaging with green light was used to create 3D stacks from which 2D surface projection were constructed to resolve stomatal apertures within the field of view. Stomatal dynamics of Chrysanthemum morifolium (Chrysanthemum) and Zea Mays (Maize) under dynamically changing light intensity were categorized, and a kinetic model was fitted to the data for quantitative comparison. In addition, we also resolved dynamics of the surface position of the leaf, related to dynamics of leaf thickness or bending. Maize stomata oscillated frequently between open and closed states under constant growth light and these oscillating stomata responded faster to changes in light than non-oscillating stomata at the same aperture. The slow closure of Chrysanthemum stomata reduced water use efficiency (WUE). Over 50% showed delayed or partial closure, leading to unnecessarily large apertures after reduced light. Stomata with larger apertures had more lag and similar closure speeds compared to those with smaller apertures and lag, further reducing WUE. In contrast, maize stomata with larger apertures closed faster, with no lag. In conclusion, our new system enables fine mapping of the heterogeneity of movement in neighboring stomata, providing new insights on the relations between stomatal dimensions, relative position and aperture changes under fluctuating light intensity.

plant biology↗

bioRxiv2023

Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae

Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organisms genotype and its environment. This study employs statistical Design of Experiments (DoE) to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted on a 168-fold improvement on pCA titer. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bio-process design efforts to unlock the full potential of microbial cell factories.

bioengineering↗

bioRxiv2023

Machine learning-guided optimization of p-coumaric acid production in yeast

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a bio-based economy. To use them effectively, appropriate connections between each phase of the cycle are crucial. Using p-coumaric acid production in Saccharomyces cerevisiea as case study, we propose the use of one-pot library generation, random screening, targeted sequencing and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of ML models strongly enable pathway optimization, and propose feature importance and SHAP values as a guide to expand the design space of original libraries. This approach led to a 68% increased production of p-coumaric acid within two DBTL cycles.

bioengineering↗

bioRxiv2023

Proteomic adaptations in the kidney reveal orchestration of local and secreted antimicrobial peptides in human pyelonephritis

Pyelonephritis (PN) is a frequent bacterial infection of the kidney and is often associated with severe diseases, organ loss and sepsis. Antibiotics are the cornerstone of therapy, however, increasing antibiotic resistance threatens therapy success and necessitates novel treatment strategies. Various proteins, such as antimicrobial peptides (AMPs), are key molecules of the innate immune response and insights into their regulation may help overcome multi-drug resistance and severe diseases. Using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS), several cellular, biological, and metabolic processes important for the antimicrobial response were identified, including a significant increase in previously undescribed proteins in human PN with antimicrobial function. Among others, we observed elevation of AMPs, such as calprotectin, azurocidin-1, and cathepsin G in the kidney, which we validated in the urine. Additionally, we observed a negative correlation of azurocidin-1 with plasma levels of C-reactive protein suggesting that the presence in the kidney may protect from severe diseases and systemic inflammation. This study represents the first renal proteomic dataset of human PN, enabling novel insights into the expression of AMPs in the context of PN. Lay SummaryGrowing antimicrobial resistance necessitates a better understanding of the expression of proteins that are critical for the immune response. Using mass spectrometry we identified AMPs in the kidney and urine of PN patients. Elevated levels of the AMP azurocidin-1 was associated with reduced systemic inflammation, indicated by lower C-reactive protein. Overall, this study identified expression of previously undescribed AMPs in the context of human PN. These proteins may play a pivotal role in protection from severe diseases and systemic inflammation.

immunology↗

bioRxiv2023

Neolactotetraosylceramide: A novel non-invasive urinary biomarker for bladder cancer

There is an urgent need to identify noninvasive biomarkers for bladder cancer. Here, we applied glycan analytics by multiplex capillary gel electrophoresis coupled to laser-induced fluorescence detection (xCGE-LIF) to unravel the global glycosphingolipid (GSL)-glycan profile of primary tumor tissues and urine samples from bladder cancer patients. Thereby, we detected neolactotetraosylceramide (Gal{beta}1-4GlcNAc{beta}1-3Gal{beta}1-4Glc-Cer, nLc4) at significantly increased levels from tumorigenic regions of bladder tissues compared to non-malignant adjacent material (n = 30). Specific expression of nLc4 in cancer tissue was confirmed by immunofluorescence staining. GSL-glycan profiling by xCGE-LIF of urinary exosomes showed that nLc4 is increased in bladder cancer patients (n = 16) when compared to controls (n = 50), with an overall sensitivity of 57% and specificity of 90%. We set-up an ELISA targeting nLc4-containing urinary exosomes from bladder cancer patients (n = 9) and cancer-free individuals (n = 9) demonstrating an overall sensitivity and specificity of 89% and 78%, respectively. SIGNFICANCEThis study shows that levels of nLc4 are significantly elevated in bladder cancer tissue and urinary exosomes of bladder cancer patients. Urinary detection of nLc4 by glycan analytics or ELISA outperforms standard diagnostic modalities, facilitating noninvasive bladder cancer diagnosis. Furthermore, nLc4 bears the potential of being a target for bladder cancer therapy.

cancer biology↗

bioRxiv2022

A hierarchical model for external electrical control of an insect, accounting for inter-individual variation of muscle force properties

Cyborg control of insect movement is promising for developing miniature, high-mobility, and efficient biohybrid robots. However, considering the inter-individual variation of the insect neuromuscular apparatus and its neural control is challenging. We propose a hierarchical model including inter-individual variation of muscle properties of three leg muscles involved in propulsion (retractor coxae), joint stiffness (pro- and retractor coxae), and stance-swing transition (protractor coxae and levator trochanteris) in the stick insect Carausius morosus. To estimate mechanical effects induced by external muscle stimulation, the model is based on the systematic evaluation of joint torques as functions of electrical stimulation parameters. A nearly linear relationship between the stimulus burst duration and generated torque was observed. This stimulus-torque characteristic holds for burst durations of up to 500 ms, corresponding to the stance and swing phase durations of medium to fast walking stick insects. Hierarchical Bayesian modeling revealed that linearity of the stimulus-torque characteristic was invariant, with individually varying slopes. Individual prediction of joint torques provides significant benefits for precise cyborg control.

systems biology↗

bioRxiv2022

Viridiplantae-specific GLXI and GLXII isoforms co-evolved and detoxify glucosone in planta

Reactive carbonyl species (RCS) such as methylglyoxal (MGO) and glyoxal (GO) are highly reactive, unwanted side-products of cellular metabolism maintained at harmless intracellular levels by specific scavenging mechanisms. MGO and GO are metabolized through the glyoxalase (GLX) system, which consists of two enzymes acting in sequence, GLXI and GLXII. While plant genomes encode a large number of different GLX isoforms, it is unclear what their specific functions are and how these arose in evolution. Here, we show that plants possess two GLX systems of different evolutionary origins and with distinct structural and functional properties. The first system is shared by all eukaryotes, scavenges MGO and GO especially during seedling establishment, and features Zn2+-type GLXI, a metal co-factor preference that arose already in the last eukaryotic common ancestor. The GLXI and GLXII of the second system can together metabolize KDG, a glucose-derived RCS, and were acquired by the last common ancestor of viridiplantae through horizontal gene transfer from proteobacteria. In contrast to bacterial GLXI homologs, which are active as dimers, plant Ni2+-type GLXI contain a domain duplication, are active as monomers, and have modified their second active site. The acquisition and neofunctionalization of a structurally, biochemically, and functionally distinct GLX systems indicate that viridiplantae are under strong selection to detoxify a diversity of RCS.

plant biology↗