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Kerkhoven, E. J.

Publications and source records attributed to Kerkhoven, E. J..

At least 19 recordsLinked to original sources

Four-species Aspergillus pan-GWAS reveals rare genome expansion in pathogenicity and contraction in domestication

Aspergillus species are ecologically diverse and deeply entangled with human health and industry. A. fumigatus and A. flavus are the two principal species of invasive aspergillosis. A. niger and A. oryzae, on the other hand, are responsible for global enzyme production, organic acid production, and koji-based fermentation industries. The question of whether these similar phenotypes share the same genomic mechanisms across the genus is not yet understood. To address this, we constructed per-species pangenomes for the four Aspergillus species (929 initial genomes filtered to 210 ANI-verified, high-quality assemblies for a total of 88 A. fumigatus, 70 A. flavus, 33 A. oryzae, and 19 A. niger assemblies) alongside a genus-level pangenome of 15,163 orthogroups, and conducted phenotype-labeled pan-genome-wide association studies (pan-GWAS) with kinship correction across all species. Pan-GWAS identified up to 117 significant orthogroup presence/absence associations per species-phenotype comparison. However, convergence analysis showed that among the 92 and 62 distinct gene families significant for human pathogenicity in A. fumigatus and A. flavus respectively, the two species seldom agreed on whether the pathogenicity was associated with the enrichment or the depletion of a specific gene family. Convergence analysis of the functional annotations also yielded zero significant results at FDR < 0.05. A literature-curated gene panel analysis also showed that a species labeled pathogenic and another labeled GRAS carried the same aflatoxin and virulence genes, suggesting that gene presence alone cannot readily explain their phenotypic differences. Instead, we propose that niche adaptation operates through the use of the pangenomic rare genome. Reclassifying rare genes by homology identified truly rare subsets (156 to 391 orthogroups per species) distinct from paralogs and gene fragments. Human-pathogenic strains showed significant rare genome expansion of 2.44-fold for both A. fumigatus and A. flavus (kinship corrected, p = 6.6 e-08). Conversely, industrial strains showed rare genome contraction where both A. niger and A. oryzae industrial strains carried 0.57-fold (kinship corrected, p = 0.015) fewer rare genes than their non-industrial counterparts. Hence, we claim that Aspergillus niche evolution proceeds through directional rare genome changes, where there is expansion under pathogenic selection, and contraction under industrial domestication. The rare genome, often discarded as noise, may represent the primary evolutionary source for clinical and biotechnological adaptation in this genus.

genomics↗

Cell-Type-Resolved Pseudobulk Classification Across Independent Cohorts Identifies Microglial PTPRG as a Transcriptional Hub in Alzheimer's Disease

Alzheimers disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and widespread cerebral pathology. Understanding cell-type-specific molecular mechanisms underlying AD is critical for identifying precise therapeutic targets. We applied a supervised machine learning approach to single-nucleus RNA sequencing data from the ROSMAP cohort, aggregating gene expression profiles into pseudobulk representations across six major brain cell types. Systematic evaluation of all possible cell-type combinations identified microglia and astrocytes as the most discriminative cell types for AD classification. A logistic regression model trained on 228 highly variable genes achieved robust classification performance on held-out ROSMAP samples (balanced accuracy 0.87, AUC 0.89) and generalized to an independent cohort from the Seattle Alzheimers Disease Brain Cell Atlas (balanced accuracy 0.86, AUC 0.92), demonstrating cross-cohort reproducibility that remains uncommon in computational AD research. Among the 72 genes selected by the model, microglial PTPRG exhibited the highest absolute coefficient. Gene Set Enrichment Analysis (GSEA) revealed that microglia-expressed genes were enriched for chronic immune activation and inflammatory signaling, while astrocyte-associated genes implicated protein homeostasis stress and HSF1-mediated chaperone pathways. Weighted Gene Co-expression Network Analysis (WGCNA) further showed that PTPRG operates within fundamentally different gene network contexts in AD and NCI microglia, with AD networks characterized by inflammatory dysregulation and NCI networks reflecting homeostatic immune surveillance. Cell-cell communication analysis identified established AD risk genes including APOE, GRN, PSEN1, and CLU among the top neuronal ligands predicted to regulate microglial PTPRG, positioning it as a convergence point for disease-relevant neuronal signals. Correlation analysis further revealed that excitatory and inhibitory neurons couple to microglial PTPRG through distinct biological processes, implicating divergent mechanisms of AD-associated microglial dysregulation. Collectively, these findings establish microglial PTPRG as a central hub integrating neuronal signaling and inflammatory dysregulation in AD pathology.

systems biology↗

AlphaGEM Enables Precise Genome-Scale Metabolic Modelling by Integrating Protein Structure Alignment with deep-learning-based Dark Metabolism Mining

Constructing high-quality genome-scale metabolic models (GEMs) for less-studied species remains challenging. To address this, we developed AlphaGEM, a versatile toolbox leveraging proteome-scale structural alignment and deep-learning-based predictions for efficient genomic mining to generate GEMs ready for applications. Our findings show that the structural alignment or protein-language-model-based prediction (i.e., PLMSearch), could identify more homologous protein relationships than sequence-blast-based alignment, contributing to the accurate profiling of metabolism from target organisms. Additionally, AlphaGEM encompasses an ensemble procedure empowered by multiple deep learning toolboxes to effectively mine the dark metabolic functions encoded by nonhomologous proteins, significantly expanding species-specific metabolic networks. We demonstrate AlphaGEMs accuracy by building GEMs for eukaryotes (e.g., S. pombe, C. albicans) and prokaryotes (e.g., K. pneumoniae, B. subtilis), achieving predictions comparable to manually curated models while outperforming existing tools. AlphaGEM also successfully reconstructs GEMs for M. musculus and C. griseus, showcasing its great potential for uncovering dark metabolism in complex mammals. Lastly, we demonstrate that AlphaGEM could facilitate the automatic GEMs reconstruction for 332 distinct yeast species with high prediction fidelity. In conclusion, AlphaGEM provides unprecedented opportunities for the precise, rapid construction of GEMs across diverse domains, which sets a solid foundation for universal functional analysis of non-model organisms having genome sequences available.

bioinformatics↗

Yeast-MetaTwin for Systematically Exploring Yeast Metabolism through Retrobiosynthesis and Deep Learning

Underground metabolism plays a crucial role in understanding enzyme promiscuity, cellular metabolism, and biological evolution, yet experimental exploration of underground metabolism is often sparse. Even though yeast genome-scale metabolic models have been reconstructed and curated for over 20 years, more than 90% of the yeast metabolome is still not covered by these models. To address this gap, we have developed a workflow based on retrobiosynthesis and deep learning methods to comprehensively explore yeast underground metabolism. We integrated the predicted underground network into the yeast consensus genome-scale model, Yeast8, to reconstruct the yeast metabolic twin model, Yeast-MetaTwin, covering 16,244 metabolites (92% of the total yeast metabolome), 2,057 metabolic genes and 59,914 reactions. We revealed that Km parameters differ between the known and underground network, identified hub molecules connecting the underground network and pinpointed the underground percentages for yeast metabolic pathways. Moreover, the Yeast-MetaTwin can predict the by-products of chemicals produced in yeast, offering valuable insights to guide metabolic engineering designs.

synthetic biology↗

Regulation of lactose and galactose growth: Insights from a unique metabolic gene cluster in Candida intermedia

Lactose assimilation is a relatively rare trait in yeasts, and Kluyveromyces yeast species have long served as model organisms for studying lactose metabolism. Meanwhile, the metabolic strategies of most other lactose-assimilating yeasts remain unknown. In this work, we have elucidated the genetic determinants of the superior lactose-growing yeast Candida intermedia. Through genomic and transcriptomic analyses and deletion mutant phenotyping, we identified three interdependent gene clusters responsible for the metabolism of lactose and its hydrolysis product galactose: the conserved LAC cluster (LAC12, LAC4) for lactose uptake and hydrolysis, the conserved GAL cluster (GAL1, GAL7, GAL10) for galactose catabolism, and a unique "GALLAC" cluster. This novel GALLAC cluster, which has evolved through gene duplication and divergence, proved indispensable for C. intermedias growth on lactose and galactose. The cluster contains the transcriptional activator gene LAC9, second copies of GAL1 and GAL10 and the XYL1 gene encoding an aldose reductase involved in carbon overflow metabolism. Notably, the regulatory network in C. intermedia, governed by Lac9 and Gal1 from the GALLAC cluster, differs significantly from the (ga)lactose regulons in Saccharomyces cerevisiae, Kluyveromyces lactis and Candida albicans. Moreover, although lactose and galactose metabolism are closely linked in C. intermedia, our results also point to important regulatory differences. This study paves the way to a better understanding of lactose and galactose metabolism in C. intermedia and provides new evolutionary insights into yeast metabolic pathways and regulatory networks. In extension, the results will facilitate future development and use of C. intermedia as a cell-factory for conversion of lactose-rich whey into value-added products.

microbiology↗

Yeast9: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

Genome-scale metabolic models (GEMs) can facilitate metabolism-focused multi-omics integrative analysis. Since Yeast8, the yeast-GEM of Saccharomyces cerevisiae, published in 2019, has been continuously updated by the community. This have increased the quality and scope of this model, culminating now in Yeast9. To evaluate its predictive performance, we generated 163 condition-specific GEMs constrained by single-cell transcriptomics from osmotic pressure or normal conditions. Comparative flux analysis showed that yeast adapting to high osmotic pressure benefits from upregulating fluxes through the central carbon metabolism. Furthermore, combining Yeast9 with proteomics revealed metabolic rewiring underlying its preference in nitrogen sources. Lastly, we created strain-specific GEMs (ssGEMs) constrained by transcriptomics for 1229 mutant strains. Well able to predict the strains growth rates, fluxomics from those large-scale ssGEMs outperformed transcriptomics in predicting functional categories for all studied genes in machine-learning models. Based on those findings we anticipate that Yeast9 will empower systems biology studies of yeast metabolism.

systems biology↗

Reprogramming Yarrowia lipolytica metabolism for efficient synthesis of itaconic acid from flask to semi-pilot scale

Itaconic acid is an emerging platform chemical with extensive applications. It is currently produced by Aspergillus terreus through biological fermentation. However, A. terreus is a fungal pathogen and needs additional morphology controls, and therefore the production remains problematic. Here, we reprogrammed the GRAS yeast Yarrowia lipolytica metabolism for competitive itaconic acid production. After redirecting the flux of lipid accumulation as carbon sink, we evaluated itaconic acid production both inside and outside the mitochondria, and fine modulated its synthetic pathway. We then mimicked the regulation of nitrogen limitation in nitrogen replete conditions through down regulation of IDH by weak promoter changing, RNAi, or CRISPRi. Ultimately, we optimized fermentation parameters for fed-batch cultivations, and produced itaconic acid with titres of 130.1 g/L in 1L bioreactors and 94.8 g/L in a 50L bioreactor on semi-pilot scale. Our finds provide effective approaches for harnessing GRAS microorganism for competitive industrial itaconic acid production.

bioengineering↗

Rewiring Saccharomyces cerevisiae metabolism for optimised Taxol(R) precursors production

Saccharomyces cerevisiae has been recognised as a convenient host for the production of early precursors to the Taxol(R) anticancer drug. Recent studies have highlighted the harmful impact of oxidative stress as a result of the activity of Taxol(R) first cytochrome P450-reductase enzymes (Taxus spp. CYP725A4-POR). Here, we evolved a new oxidative stress-tolerant yeast strain on galactose, which led to a three-fold higher titre of the CYP725A4 enzyme substrate, taxadiene. We comprehensively analysed the performance of the evolved and parent strain in galactose-limited chemostat cultures before and during oxidative stress induction. Integrating the transcriptomics and metabolite profiling data in an enzyme-constrained genome scale model enabled a more accurate prediction of changes that occurred to biological pathways as a response to/consequence of evolution and oxidative stress. The analyses showed a better performance of the evolved strain with improved respiration and reduced overflow metabolites production. The strain was robust to re-introduction of the oxidative stress, potentially due to the cross-protection mechanism, which contributed to likely better heme, flavin and NADPH availability for an optimal expression of CYP725A4 and POR in yeast. The increased level of taxadiene production has potentially occurred due to the antioxidant properties of taxadiene or as a mechanism to overcome the toxicity of geranylgeranyl diphosphate, the precursor to taxadiene synthase. HighlightsO_LIThe antioxidant properties of taxadiene promotes its production in Saccharomyces cerevisiae C_LIO_LIS. cerevisiae ALE on H2O2 and galactose regulates Flavin, iron and NADPH metabolism as well as carbon and protein recycling pathways through cross-protection and anticipation mechanisms C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/543533v1_fig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1758c64org.highwire.dtl.DTLVardef@543681org.highwire.dtl.DTLVardef@c67339org.highwire.dtl.DTLVardef@b6c56e_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Graphical abstract of the study. Figure was created with BioRender.com. C_FIG

synthetic biology↗

Abolishing storage lipids induces protein misfolding and stress responses in Yarrowia lipolytica

Summary/AbstractYarrowia lipolytica naturally saves carbon excess as storage lipids. Engineering efforts allow redirecting the high precursor flux required for lipid synthesis towards added-value chemicals such as polyketides, flavonoids, and terpenoids. To redirect precursor flux from storage lipids to other products, four genes involved in triacylglycerol and sterol ester synthesis (DGA1, DGA2, LRO1, ARE1) can be deleted. To elucidate the effect of the deletions on cell physiology and regulation, we performed chemostat cultivations under carbon and nitrogen limitation, followed by transcriptome analysis. We found that storage lipid-free cells show an enrichment of the unfolded protein response, and several biological processes related to protein refolding and degradation are enriched. Additionally, storage lipid-free cells show an altered lipid class distribution with an abundance of potentially cytotoxic free fatty acids under nitrogen limitation. Our findings not only highlight the importance of lipid metabolism on cell physiology and proteostasis, but can also aid the development of improved chassy strains of Y. lipolytica for commodity chemical production. HighlightsO_LIPhysiological and transcriptomic characterization of storage lipid free (Q4) strain C_LIO_LIStorage lipid free strain shows an increased free fatty acid fraction on nitrogen limitation C_LIO_LIStorage lipid free strain is more sensitive towards fatty acid supplementation C_LIO_LIUnfolded protein response, chaperones, and ubiquitin are enriched in the storage lipid free strain C_LI

systems biology↗

Resin acids play key roles in shaping microbial communities during degradation of spruce bark

The bark is the outermost defense of trees against microbial attack, largely thanks to toxicity and prevalence of extractive compounds. Nevertheless, bark decomposes in nature, though by which species and mechanisms remains unknown. Here, we have followed the development of microbial enrichments growing on spruce bark over six months, by monitoring both chemical changes in the material and performing community and metagenomic analyses. Carbohydrate metabolism was unexpectedly limited, and instead a key activity was metabolism of extractives. Resin acid degradation was principally linked to community diversification with specific bacteria revealed to dominate the process. Metagenome-guided isolation facilitated the recovery of the dominant enrichment strain in pure culture, which represents a new species (Pseudomonas abieticivorans sp. nov.), that can grow on resin acids as a sole carbon source. Our results illuminate key stages in degradation of an abundant renewable resource, and how defensive extractive compounds have major roles in shaping microbiomes.

microbiology↗

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

systems biology↗

Genome scale modeling of the protein secretory pathway reveals novel targets for improved recombinant protein production in yeast

Eukaryal cells are used for the production of many recombinant pharmaceutical proteins, including several of the current top-selling products. The protein secretory pathway in eukaryal cells is complex and involves many different processes such as post-translational modifications, translocation, and folding. Furthermore, recombinant protein production competes with native secretory proteins for the limited energy and proteome resources allocated to the protein secretory pathway. Due to the complexity of this pathway, improvement through metabolic engineering has traditionally been relatively ad-hoc; and considering the industrial importance of this pathway, there is a need for more systematic approaches for novel design principles. Here, we present the first proteome-constrained genome-scale protein secretory model of a eukaryal cell, namely for the yeast Saccharomyces cerevisiae (pcSecYeast). The model contains all key processes of this pathway, i.e., protein translation, modification, and degradation coupled with metabolism. The model can capture delicate phenotypic changes such as the switch in the use of specific glucose transporters in response to changing extracellular glucose concentration. Furthermore, the model can also simulate the effects of protein misfolding on cellular growth, suggesting that retro-translocation of misfolded proteins contributes to protein retention in the Endoplasmic reticulum (ER). We used pcSecYeast to simulate various recombinant proteins production and identified overexpression targets for different recombinant proteins overproduction. We experimentally validated many of the predicted targets for -amylase production in this study, and the results show that the secretory pathways have more limited capacity than metabolism in terms of protein secretion.

systems biology↗

Deep learning based kcat prediction enables improved enzyme constrained model reconstruction

Enzyme turnover numbers (kcat values) are key parameters to understand cell metabolism, proteome allocation and physiological diversity, but experimentally measured kcat data are sparse and noisy. Here we provide a deep learning approach to predict kcat values for metabolic enzymes in a high-throughput manner with the input of substrate structures and protein sequences. Our approach can capture kcat changes for mutated enzymes and identify amino acid residues with great impact on kcat values. Furthermore, we applied the approach to predict genome scale kcat values for over 300 yeast species, demonstrating that the predicted kcat values are consistent with current evolutional understanding. Additionally, we designed an automatic pipeline using the predicted kcat values to parameterize enzyme-constrained genome scale metabolic models (ecGEMs) facilitated by a Bayesian approach, which outperformed the default ecGEMs in predicting phenotypes and proteomes and enabled to explain phenotype differences among yeast species. The deep learning kcat prediction approach and automatic ecGEM construction pipeline would thus be a valuable tool to uncover the global trend of enzyme kinetics and physiological diversity, and to further elucidate cell metabolism on a large scale.

systems biology↗

Reconstruction of genome-scale metabolic model for Hansenula polymorpha using RAVEN toolbox

Genome scale metabolic models (GEMs) provide a useful framework for modeling the metabolism of microorganisms. While the applications of GEMs are wide and far reaching, the reconstruction and continuous curation of such models can be perceived as a tedious and time consuming task. Using the RAVEN toolbox, this protocol practically demonstrates how researchers can create their own GEMs using a homology based approach. To provide a complete example, we reconstruct a draft GEM for the industrially relevant yeast Hansenula polymorpha.

systems biology↗

Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into GEMs was first enabled by the GECKO method, allowing the study of phenotypes constrained by protein limitations. Here, we upgraded the GECKO toolbox in order to enhance models with enzyme and proteomics constraints for any organism with an available GEM reconstruction. With this, enzyme-constrained models (ecModels) for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus were generated, aiming to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions revealed that upregulation and high saturation of enzymes in amino acid metabolism were found to be common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO was further developed by the implementation of an automated framework for continuous and version-controlled update of ecModels, which was validated by producing additional high-quality ecModels for Escherichia coli and Homo sapiens. These efforts aim to facilitate the utilization of ecModels in basic science, metabolic engineering and synthetic biology purposes.

systems biology↗

Genome-scale Model Constrained by Proteomics Reveals Metabolic Rearrangements in the Heterologous Host Streptomyces coelicolor

Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.

systems biology↗

FALCONET: an R package to accelerate automatic visualisation of genome scale metabolic models

SummaryFALCONET (FAst visuaLisation of COmputational NETworks) enables the automatic for-mation and visualisation of metabolic maps from genome-scale models with R and CellDesigner, readily facilitating the visualisation of multi-layers omics datasets in the context of metabolic networks.\n\nMotivationUntil now, numerous GEMs have been reconstructed and used as scaffolds to conduct integrative omics analysis and in silico strain design. Due to the large network size of GEMs, it is challenging to produce and visualize these networks as metabolic maps for further in-depth analyses.\n\nResultsHere, we presented the R package - FALCONET, which facilitates drawing and visualizing metabolic maps in an automatic manner. This package will benefit the research community by allowing a wider use of GEMs in systems biology.\n\nAvailability and implementationFALCONET is available on https://github.com/SysBioChalmers/FALCONET and released under the MIT License.\n\nContactnielsenj@chalmers.se\n\nSupplementary informationSupplementary data are available online.

systems biology↗