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Nielsen, J.

Publications and source records attributed to Nielsen, J..

At least 37 records · Page 2Linked to original sources

Model-Driven Elucidation of Lactose and Galactose Metabolism via Oxidoreductive Pathway in Sungouiella intermedia for Cell Factory Applications

Converting industrial side streams into value-added chemicals using microbial cell factories is of increasing interest, as such processes offer solutions to reduce waste and production costs. However, developing new, efficient cell factories for precision fermentation remains challenging due to limited knowledge about their metabolic capabilities. Here, we investigate the lactose and galactose metabolism of the non-conventional yeast Sungouiella intermedia (formerly Candida intermedia), using knowledge-matching of high-quality genome-scale metabolic model (GEM) with extensive experimental analysis and determine its potential as a future cell factory on lactose-rich industrial side-streams. We show that this yeast possesses the conserved Leloir pathway as well as an oxidoreductive galactose catabolic route. Contextualization of RNAseq data into Sint-GEM highlights the regulatory mechanisms on the oxidoreductive pathway and how this pathway can enable adaptation to diverse environments. Model simulations, together with experimental data from continuous and batch bioreactors, indicate that S. intermedia uses upstream enzymes of the oxidoreductive pathway, in a condition-dependent manner, and produce the sugar alcohol galactitol as a carbon overflow metabolite, coupled to redox co-factor balancing during both lactose and galactose growth. Furthermore, the new metabolic insights facilitated the development of an improved bioprocess design, where an engineered S. intermedia strain could achieve galactitol yields of >90% of the theoretical maximum at improved production rates using the industrial side-stream cheese whey permeate as feedstock. Additional strain engineering resulted in galactitol-to-tagatose conversion, proving the versatility of the future production host. Overall, this work sheds new light on the intrinsic interplay between parallel metabolic pathways that shape the lactose and galactose catabolism in S. intermedia. It also demonstrates how a GEM combined with experimental analysis can work in synergy to fast-forward metabolic characterization and development of new, non-conventional yeast cell factories. HighlightsO_LIAn oxidoreductive pathway functions in concert with the Leloir pathway for galactose catabolism. C_LIO_LIGEM predicts that galactitol secretion enables efficient carbon overflow metabolism and maintains redox balance. C_LIO_LIKnowledge-matching of GEM with experimental results highlights cell factory potential. C_LIO_LIHigh galactitol yields and proof-of-concept tagatose production using whey permeate as feedstock. C_LI

bioengineering↗

Effect of high-altitude exposure on skeletal muscle mitochondrial subcellular distribution, ultrastructure and respiration in sea-level residents

Endurance exercise performance is associated with a well-developed skeletal muscle mitochondrial network, which is composed of subsarcolemmal mitochondria interconnected with intermyofibrillar mitochondria bending around the myofibrils. High-altitude exposure is typically incorporated in elite sport training regimens, but little is known about how this network adapts to an environment characterised by tissue hypoxia. For this reason, we investigated how high-altitude exposure affects mitochondrial subcellular distribution, ultrastructure, respiratory control, and intrinsic mitochondrial respiratory capacity. Nine healthy and recreationally active sea-level residents (eight males and one female) resided at an altitude of 3454 m with biopsies collected from the vastus lateralis muscle before and after 7 and 28 days at high altitude. The muscular mitochondrial volume density (MitoVD) increased after high-altitude exposure, driven by an increase in the intermyofibrillar MitoVD. This was however accompanied by a decreased cristae surface area per skeletal muscle fibre volume (MuscularCD) because of a decline in the cristae surface area per mitochondrial volume (MitoCD). Despite a reduced MuscularCD, mass-specific maximal coupled respiration (OXPHOS_CII+CI+ETF) increased slightly, and was considerably elevated when normalised to MuscularCD, suggesting intrinsic adaptations to high altitude. The difference between cristae-specific OXPHOS_CII+CI+ETF and an associated cristae-specific leak respiration (Leak_CII+CI+ETF) indicated a markedly higher degree of coupling between the electron flow in the electron transport system and ATP production. As the effect size 95% confidence intervals includes trivial effects the results need to be substantiated. In conclusion, high-altitude exposure altered mitochondrial subcellular distribution, ultrastructure and induced intrinsic respiratory adaptations.

physiology↗

Leveraging large language models for metabolic engineering design

Establishing efficient cell factories involves a continuous process of trial and error due to the intricate nature of metabolism. This complexity makes predicting effective engineering targets a challenging task. Therefore, it is vital to learn from the accumulated successes of previous designs for advancing future cell factory development. In this study, we developed a method based on large language models (LLMs) to extract metabolic engineering strategies from research articles on a large scale. We created a database containing over 29006 metabolic engineering entries, 1210 products and 751 organisms. Using this extracted data, we trained a hybrid model combining deep learning and mechanistic approaches to predict engineering targets. Our model outperformed traditional metabolic engineering target prediction algorithms, excelled in predicting the effects of gene modifications, and generalized well to out-of-distribution products and multiple gene combinations. Our study provides a valuable dataset, a chatbot, and an engineering target prediction model for the metabolic engineering field and exemplifies an efficient method for leveraging existing knowledge for future predictions.

systems biology↗

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↗

Extensively acquired antimicrobial resistant bacteria restructure the individual microbial community in post-antibiotic conditions

In recent years, the overuse of antibiotics has led to the emergence of antimicrobial resistant (AMR) bacteria. To evaluate the spread of AMR bacteria, the reservoir of AMR genes (resistome) has traditionally been identified from environmental samples, hospital environments, and human populations; however, the functional role of AMR bacteria in the human gut microbiome and their persistency within individuals has not been fully investigated. Here, we performed a strain-resolved in-depth analysis of the resistome changes by reconstructing a large number of metagenome-assembled genomes (MAGs) of antibiotics- treated individuals gut microbiome. Interestingly, we identified two bacterial populations with different resistome profiles, extensively acquired antimicrobial resistant bacteria (EARB) and sporadically acquired antimicrobial resistant bacteria (SARB), and found that EARB showed broader drug resistance and a significant functional role in shaping individual microbiome composition after antibiotic treatment. Furthermore, longitudinal strain analysis revealed that EARB bacteria were inherently carried by individuals and can reemerge through strain switching in the human gut microbiome. Our data on the presence of AMR bacteria in the human gut microbiome provides a new avenue for controlling the spread of AMR bacteria in the human community.

genomics↗

Yeast adapts to diverse ecological niches driven by genomics and metabolic reprogramming

The famous model organism - Saccharomyces cerevisiae is widely present in a variety of natural and human-associated habitats. Despite extensive studies of this organism, the metabolic mechanisms driving its adaptation to varying niches remain elusive. We here gathered genomic resources from 1,807 S. cerevisiae strains and assembled them into a high-quality pan-genome, facilitating the comprehensive characterization of genetic diversity across isolates. Utilizing the pan-genome, 1,807 strain-specific genome-scale metabolic models (ssGEMs) were generated, which performed well in quantitative predictions of cellular phenotypes, thus helping to examine the metabolic disparities among all S. cerevisiae strains. Integrative analyses of fluxomic and transcriptomics with ssGEMs showcased the ubiquitous transcriptional regulation in certain metabolic sub-pathways (i.e., amino acid synthesis) at a population level. Additionally, the gene/reaction loss analysis through the ssGEMs refined by transcriptomics showed that S. cerevisiae strains from various ecological niches had undergone reductive evolution at both the genomic and metabolic network levels when compared to wild isolates. Finally, the compiled analyses of the pan-genome, transcriptome, and metabolic fluxome revealed remarkable metabolic differences among S. cerevisiae strains originating from distinct oxygen-limited niches, including human gut and cheese environments, and identified convergent metabolic evolution, such as downregulation of oxidative phosphorylation pathways. Together, these results illustrate how yeast adapts to distinct niches modulated by genomic and metabolic reprogramming, and provide computational resources for translating yeast genotype to fitness in future studies.

systems biology↗

The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of Evolution

The functions of cells and proteins depend on their biochemical microenvironment. To understand how biochemical constraints shaped protein structural evolution, we coupled the extensive genetic and metabolic data from the Saccharomycotina subphylum with the capability of AlphaFold2 to systematically predict protein structures from sequence. Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organisms overall metabolism; the topological organisation of the metabolic network; and each enzymes molecular properties. For example, structural evolution depends on each enzymes reaction mechanism, on the variability rather than the amount of metabolic flux, and on biosynthetic cost. Evolutionary cost-optimization is stronger on highly abundant enzymes and acts differently on different structural domains, with the exception of small-molecule binding sites, which are prioritised over other structural domains and lack cost-optimisation. Finally, while enzyme surfaces are less constrained, surface residues can also be exposed to positive selection for the co-evolution of protein-protein interaction sites. Accessing AlphaFolds power to predict protein structures systematically and across species barriers, facilitating the integration of protein structures with functional genomics, we were thus able to map biological constraints which shape protein structural evolution at scale and over long timelines.

systems biology↗

The Molecular Landscape of Cellular Metal Ion Biology

Metal ions play crucial roles in cells, yet the broader impact of metal availability on biological networks remains underexplored. We generated genome-wide resources, systematically quantifying yeast cell growth, metallomic, proteomic, and genetic responses upon varying each of its essential metal ions (Ca, Cu, Fe, K, Mg, Mn, Mo, Na, Zn), over several orders of magnitude. We find that metal ions deeply impact cellular networks, with 57.6% of the proteome, including most signalling pathways, responding. While the biological response to each metal is distinct, our data reveals common properties of metal responsiveness, such as concentration interdependencies and metal homeostasis. We describe a compendium of metal-dependent cellular processes and reveal that several understudied genes can be functionally annotated based on their metal responses. Furthermore, we report that metalloenzymes occupy central nodes in the metabolic network and are more likely to be encoded by isozymes, resulting in system-wide responsiveness to metal availability.

systems biology↗

Proinflammatory Cytokines, but not Glucolipotoxicity, Suppress Nonsense-Mediated RNA Decay to Impair Regulated Transcript Isoform Processing in Pancreatic β-Cells

Proinflammatory cytokines are implicated in pancreatic {beta}-cell failure in type 1 and type 2 diabetes and are known to stimulate alternative RNA splicing and the expression of Nonsense-Mediated RNA Decay (NMD) components. Here, we investigate whether cytokines regulate NMD activity and identify transcript isoforms targeted in {beta}-cells. A luciferase-based NMD reporter transiently expressed in rat INS1(832/13), human-derived EndoC-{beta}H3 or dispersed human islet cells is used to examine the effect of proinflammatory cytokines (Cyt) on NMD activity. Gain- or loss-of function of two key NMD components UPF3B and UPF2 is used to reveal the effect of cytokines on cell viability and function. RNA-sequencing and siRNA-mediated silencing are deployed using standard techniques. Cyt attenuate NMD activity in insulin-producing cell lines and primary human {beta}-cells. These effects are found to involve ER stress and are associated with downregulation of UPF3B. Increases or decreases in NMD activity achieved by UPF3B overexpression (OE) or UPF2 silencing, raises or lowers Cyt-induced cell death, respectively, in EndoC-{beta}H3 cells, and are associated with decreased or increased insulin content, respectively. No effects of these manipulations are observed on glucose-stimulated insulin secretion. Transcriptomic analysis reveals that Cyt increase alternative splicing (AS)-induced exon skipping in the transcript isoforms, and this is potentiated by UPF2 silencing. Gene enrichment analysis identifies transcripts regulated by UPF2 silencing whose proteins are localized and/or functional in extracellular matrix (ECM) including the serine protease inhibitor SERPINA1/-1-antitrypsin, whose silencing sensitises {beta}-cells to Cyt cytotoxicity. Cytokines suppress NMD activity via UPR signalling, potentially serving as a protective response against Cyt-induced NMD component expression. Our findings highlight the central importance of RNA turnover in {beta}-cell responses to inflammatory stress.

cell biology↗

Specific inhibition of α-synuclein oligomer generation and toxicity by the chaperone domain Bri2 BRICHOS

Understanding the molecular mechanisms of neurodegenerative diseases and finding efficient treatments have been major priorities for research and society, yet new therapeutic approaches remain essential to face the socio-economic burden caused by these devastating diseases. Protein misfolding and aggregation are involved in several neurodegenerative disorders, such as -synuclein (Syn) implicated in Parkinsons disease. Elucidating the microscopic nucleation mechanisms has opened new opportunities to develop therapeutics against toxic mechanisms and species. Here, we show that naturally occurring molecular chaperones, represented by the anti-amyloid Bri2 BRICHOS domain, can be used to target Syn-associated nucleation processes and structural species related to neurotoxicity. Our findings revealed that BRICHOS predominately suppresses the formation of new nucleation units on the fibrils surface (secondary nucleation), in addition to fibril-end elongation. This mechanism implies a drastic decrease of the oligomer generation rate. Besides targeting secondary nucleation sites on the fibril surface, BRICHOS directly binds to oligomeric Syn species. Further, using ex vivo experiments, BRICHOS effectively diminishes Syn fibril-related toxicity to hippocampal electrophysiology. Our studies show that molecular chaperones can be utilized as tools to target molecular processes and structural species related to Syn neurotoxicity and have the potential as protein-based treatments against neurodegenerative disorders.

biophysics↗

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↗

The mitochondrial mRNA stabilizing protein, SLIRP, regulates skeletal muscle mitochondrial structure and respiration by exercise-recoverable mechanisms

Summary and graphical abstractDecline in mitochondrial function associates with decreased muscle mass and strength in multiple conditions, including sarcopenia and type 2 diabetes. Optimal treatment could include improving mitochondrial function, however, there are limited and equivocal data regarding the molecular cues controlling muscle mitochondrial plasticity. Here we uncover the mitochondrial-mRNA-stabilizing protein SLIRP, in complex with LRPPRC, as a PGC-1 target that regulates mitochondrial structure, respiration, and mitochondrially-encoded-mRNA pools in skeletal muscle. Exercise training effectively counteracted mitochondrial defects induced by loss of LRPPRC/SLIRP, despite sustained low mitochondrially-encoded-mRNA pools, via increased mitoribosome translation capacity. In humans, exercise training robustly increased muscle SLIRP and LRPPRC protein content across exercise modalities and sexes, yet this increase was less prominent in subjects with type 2 diabetes. Our work identifies a mechanism of post-transcriptional mitochondrial regulation in skeletal muscle through mitochondrial mRNA stabilization. It emphasizes exercise as an effective approach to alleviate mitochondrial defects by possibly increasing mitoribosome capacity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/564600v2_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1cdc6c5org.highwire.dtl.DTLVardef@b4cb1borg.highwire.dtl.DTLVardef@182871corg.highwire.dtl.DTLVardef@7462ea_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

Differential utilisation of subcellular skeletal muscle glycogen pools: A comparative analysis between 1 and 15 minutes of maximal exercise

Distinct subcellular pools of glycogen particles exist within skeletal muscle fibres, distributed both within and between myofibrils and can be found in proximity to, or at a distance from mitochondria. Their precise localisation may influence their degradation rate and role in muscle function. Here, we investigated how exercise at different intensities (1- and 15-min maximal exercise) with known variations in glycogenolytic rate and relative contribution from anaerobic metabolism affects the utilisation of the distinct pools. Further, we investigated how lowered carbohydrate and energy intake affected glycogen volume densities and the storage of glycogen particles (i.e., localisation, size, and number) and their subsequent utilisation during the exercises. Using a randomized, counterbalanced, cross-over design, participants performed two maximal cycle tests of either 1 (n=10) or 15 min (n=10), conducted following consumption of two distinct diets with either high or lowered carbohydrate and energy contents. Muscle biopsies from m. vastus lateralis were obtained before and after the exercises. Intermyofibrillar glycogen was preferentially utilised during the 1-min exercise, whereas intramyofibrillar glycogen was preferentially utilised during the 15-min exercise. The lowered carbohydrate and energy intake decreased the particle size across all subcellular pools and reduced the numerical density in the intramyofibrillar and subsarcolemmal pools, with no effects on the glycogen utilisation during the subsequent exercise. In conclusion, the distinct subcellular glycogen pools were differentially utilised during 1-min and 15-min maximal exercise. Additionally, lowered carbohydrate and energy consumption reduces particle size and numerical density, depending on subcellular localisation.

physiology↗

Biosensor-Assisted Laboratory Evolution of Malonyl-CoA production in Saccharomyces cerevisiae

The production of bio-based chemicals and fuels through microbial engineering offers a promising and sustainable alternative to petroleum-based fuels and chemicals, with the potential for scalability. However, engineering microbes and continuously evolving them to enhance the production of industrially relevant products is a complex and challenging task, requiring precise selection of genetic traits to achieve desired outcomes. In this study, we report the development of a novel counter-selectable growth-sensitive malonyl-CoA platform strain by coupling the malonyl-CoA repressor FapR from Bacillus subtilis to essential gene promoters involved in glucose growth and the plasma membrane arginine permease. This platform strain was then coupled with a CRISPR-dCas9 guide-RNA (gRNA) library, which after multiple rounds of dilutions and library sequencing, resulted in the enrichment for gRNAs that increased fluxes towards malonyl-CoA. The enriched gRNAs were validated for their effects on growth enhancement, gene regulation, and the production of an industrially relevant malonyl-CoA product, namely 3-hydroxypropionic acid. This study highlights an innovative approach to microbial engineering and opens up avenues for further exploration in the field of laboratory continuous evolution.

bioengineering↗

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↗

Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast

Development of efficient cell factories that can compete with traditional chemical production processes is complex and generally driven by case-specific strategies, based on the product and microbial host of interest. Despite major advancements in the field of metabolic modelling in recent years, prediction of genetic modifications for increased production remains challenging. Here we present a computational pipeline that leverages the concept of protein limitations in metabolism for prediction of optimal combinations of gene engineering targets for enhanced chemical bioproduction. We used our pipeline for prediction of engineering targets for 102 different chemicals using Saccharomyces cerevisiae as a host. Furthermore, we identified sets of gene targets predicted for groups of multiple chemicals, suggesting the possibility of rational model-driven design of platform strains for diversified chemical production. One sentence summaryNovel strain design algorithm ecFactory on top of enzyme-constrained models provides unprecedented chances for rational strain design and development.

systems biology↗

Increased mitochondrial surface area and cristae density in the skeletal muscle of strength athletes

Mitochondria are the cellular organelles responsible for resynthesising the majority of ATP. In skeletal muscle, there is an increased ATP turnover during resistance exercise to sustain the energetic demands of muscle contraction. Despite this, little is known regarding the mitochondrial characteristics of chronically strength-trained individuals and any potential pathways regulating the strength-specific mitochondrial remodelling. Here, we investigated the mitochondrial structural characteristics in skeletal muscle of strength athletes and age-matched untrained controls. The mitochondrial pool in strength athletes was characterised by increased mitochondrial cristae density, decreased mitochondrial size, and increased surface-to-volume ratio, despite similar mitochondrial volume density. We also provide a fibre-type and compartment specific assessment of mitochondria morphology in human skeletal muscle, which reveals across groups a compartment-specific influence on mitochondrial morphology that is largely independent of fibre-type. Furthermore, we show that resistance exercise leads to signs of mild mitochondrial stress, without an increase in the number of damaged mitochondria. Using publicly available transcriptomic data we show that acute resistance exercise increases the expression of markers of mitochondrial biogenesis, fission, and mitochondrial unfolded protein responses (UPRmt). Further, we observed an enrichment of the UPRmt in the basal transcriptome of strength-trained individuals. Together, these findings show that strength athletes possess a unique mitochondrial remodelling, which minimises the space required for mitochondria. We propose that the concurrent activation of markers of mitochondrial biogenesis and mitochondrial remodelling pathways (fission and UPRmt) with resistance exercise may be partially responsible for the observed mitochondrial phenotype of strength athletes.

physiology↗

Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport

Energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons. Here we used metabolic models that estimate costs of enzyme usage per pathway, enzyme utilization over time, and enzyme transport to evaluate a paradigm that suggests that brain energy metabolism is optimized to minimize enzyme synthesis and transportation costs. Our models recapitulate known metabolic behaviors and provide explanation for the astrocyte-neuron lactate shuttle theory.

systems biology↗