Biology subjects

Jouhten, P.

Publications and source records attributed to Jouhten, P..

14 recordsLinked to original sources

bioRxiv2026

ActSeekN: A Structural-Motif-Based Pipeline for Interpretable Enzyme Function Annotation

Accurate enzyme function annotation remains a major bottleneck in genome analysis despite the rapid expansion of available protein sequence and structure data. Most existing methods rely on sequence similarity or machine-learning representations, which often perform poorly for proteins with low sequence identity or convergent evolutionary histories. Because enzymatic activity is determined by the three-dimensional arrangement of catalytic and binding-site residues, structure-based approaches offer a mechanistically grounded alternative. However, their broader application has been constrained by the limited size and coverage of curated active-site reference databases. To address this challenge, we developed ActSeekN, a structural-motif-based functional annotation pipeline that combines the ActSeek active-site search algorithm with a newly constructed large-scale reference database derived from AlphaFold-predicted structures, UniProt annotations, and curated catalytic residue information. This framework enables rapid and scalable identification of conserved catalytic motifs across structurally related proteins, allowing function to be transferred on the basis of local three-dimensional catalytic geometry rather than global sequence similarity. In this way, ActSeekN overcomes a central limitation of previous structure-based methods by expanding the searchable space of catalytic motifs while retaining mechanistic interpretability. Benchmarking against state-of-the-art machine-learning approaches demonstrates competitive or superior performance. Applications to yeast, human, and Trichoderma reesei proteomes refine existing annotations, complete partial EC assignments, and identify previously unrecognized enzymatic functions, highlighting ActSeekN as a powerful tool for genome annotation and biotechnology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/720574v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@f5da16org.highwire.dtl.DTLVardef@c0faa4org.highwire.dtl.DTLVardef@18765fforg.highwire.dtl.DTLVardef@3960b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

In silico prediction of metabolic trait robustness in microbial cells

In industrial applications, microbial strains undergo notable biomass expansion subjecting them to Darwinian selection. The consequent adaptive evolution may threaten the often tediously developed desired traits of the strains, such as flavor or platform chemical production. Yet, it remains unresolved how to predict the evolutionary trait robustness. Here, we propose TRAEV (Trait Robustness Against EVolution), a computational framework for in silico prediction of evolutionary trajectories and robustness of desired metabolic traits in application environments. TRAEV uses constraint-based metabolic model simulations to predict environment-dependent trait-fitness dependencies and Monte Carlo-based perturbation analysis to account for the stochasticity of adaptive evolution. First, TRAEV predicts the immediate phenotypic adaptation to the new environment, and then, the evolutionary trajectories of fitness and desired trait by sampling enzyme usage changes. From the predicted trajectories, trait robustness is quantified as two scores: Robustness Score (RS) and Trade-off Score (TS). RS and TS are the mean of a normalized desired trait and the mean of the product of normalized changes in the desired trait and in fitness, respectively, over intermediate metabolic states along the evolutionary trajectory. We validated TRAEV by demonstrating that it predicted the relative robustness of heterologous pigmentation of genetically engineered Saccharomyces cerevisiae strains in synthetic defined chemical environments aligned with experimental observations. We then further applied TRAEV to predictively assess the robustness of desired aroma generation trait in a wine must environment by multiple S. cerevisiae strains if they were developed via a laboratory selection process. Thus, we showed how TRAEV predictions could guide such strain development. TRAEV can be integrated into computational strain design workflows across microbial strains, metabolic traits, and application environments. Ultimately, model-predicted evolutionary robustness of desired traits can guide both strain and process development and help avoiding production losses and enhancing the economic attractiveness of industrial applications using microbial cells. Author summaryMicrobial cells developed to express desired traits by genetic engineering or selection processes are used in a wide range of applications, from biotechnological chemical production to food and beverage fermentations. However, when the cells replicate in the application environment the traits are subject to Darwinian selection and consequent adaptive evolution. As a result, desired traits can be rapidly lost. To mitigate such undesired evolutionary changes, we developed TRAEV (Trait Robustness Against EVolution), a computational framework for in silico assessment of evolutionary trajectories and robustness of desired traits in application environments by integrating constraint-based modeling and Monte Carlo-based perturbation analysis methods. We demonstrate the usability of TRAEV by validating its predictions with experimental data on the robustness of heterologous pigmentation of engineered Saccharomyces cerevisiae strains, and applying it to predict the robustness of aroma generation in wine must by multiple S. cerevisiae strains if they were developed by laboratory selection in specific conditions. As being applicable across microbial strains, metabolic traits, and application environments, we believe that TRAEV can help to avoid production losses, and thus, contribute to the development of economically attractive industrial applications using microbial cells.

evolutionary biology↗

bioRxiv2026

Spontaneous mutation rate in Saccharomyces cerevisiae is lower in nutritional and genotypic conditions reducing specific growth rate

Even in the absence of mutagenic factors, spontaneous errors in DNA replication generate genetic diversity in microbial populations. The spontaneous mutation rate is not immutable but may be conditionally elevated. However, it remains unresolved whether apparently non-stressful nutritional or genotypic conditions affect the spontaneous mutation rate. Here, we determined the spontaneous mutation rate of haploid Saccharomyces cerevisiae CEN.PK113-7D in three nutritional and three genotypic conditions. The nutritional and genotypic conditions influenced the specific growth rate of the S. cerevisiae population. Thus, we extended the established fluctuation assay for spontaneous mutation rate determination with CAN1 as a reporter gene to populations with different generation times. We applied the method to determine the spontaneous mutations rates in wild type S. cerevisiae grown on glucose and ammonium, raffinose and ammonium, and glucose and L-tryptophan as sole carbon and nitrogen sources. Alike we determined the spontaneous mutation rates of two engineered S. cerevisiae strains (i.e., representing genotypic conditions different from wild type) on glucose and ammonium medium. The engineered strains had two to three heterologous or variant genes integrated into the genome, common to simple heterologous small molecule production host strains. In all alternative nutritional and genotypic conditions, the spontaneous mutation rate of S. cerevisiae was reduced compared to wild type growing on glucose and ammonium medium. Spontaneous mutation rate is fundamentally relevant for evolvability of strains, but it may also influence the performance robustness of microbial populations in applications such as food or beverage fermentation or biotechnological chemical production. Our novel findings are important for biotechnological processes using microbial cells, often engineered and cultivated in unnatural chemical environments.

evolutionary biology↗

bioRxiv2025

Influence of ploidy and genetic background on stress tolerance of intraspecific yeast hybrids

Hybrid vigor, or heterosis, is widely exploited in yeast strain improvement. Yet, how ploidy and genetic background jointly shape heterosis across industrially relevant stresses remains unclear. Here, we generated 1023 Saccharomyces cerevisiae intraspecific hybrids derived from 18 genetically diverse parents, using two different approaches, yielding sets of hybrids with variable ploidy for the same parental combinations. High-throughput growth assays in media with five stress conditions (14% ethanol, 1.5 M NaCl, 0.15 M lactic acid, 0.05 M acetic acid, 0.05 M HMF) revealed extensive heterosis across 6138 hybrid-condition combinations. Most combinations displayed mid-parent heterosis and over a third exceeded the best parent, with the strongest gains during growth in the presence of 14% ethanol and 1.5 M NaCl. Increasing ploidy was generally associated with reduced growth and reduced best-parent heterosis, whereas greater predicted hybrid heterozygosity or genetic distance between parents was positively associated with heterosis in the presence of 14% ethanol and 1.5 M NaCl. Domestication status also affected these trends, as crosses between two domesticated strains tended to perform better in the presence of ethanol and NaCl, while crosses between two wild strains grew best in control conditions and in the presence of acetic acid. Together, these results demonstrate condition-dependent contributions of ploidy and parentage to heterosis and provide targeted breeding strategies for the improvement of stress-tolerance in industrial yeasts.

microbiology↗

bioRxiv2025

Compound-specific DNA adduct profiling with nanopore sequencing and IonStats

AO_SCPLOWBSTRACTC_SCPLOWCovalently bound DNA adducts are mutation precursors that contribute to aging and diseases such as cancer. Accurate detection of adducts in the genome will shed light on tumorigenesis. Commonly used detection methods are unable to pinpoint the exact genomic locations of adducts. Long-read nanopore sequencing has the potential to accurately detect multiple types of DNA adducts at single-nucleotide precision. In this study, we developed a novel statistical toolkit, IonStats, to profile DNA adducts in nanopore sequencing data. With IonStats, we investigated the effects of four adduct-inducing genotoxic compounds on nanopore sequencing, and found both shared and compound-specific perturbations in base quality scores, ionic current profiles, and translocation dynamics. Notably, aristolochic acid II and melphalan treatments profoundly altered nanopore readouts and led to substantial sequence-specific read interruptions. Our study shows that nanopore sequencing can be effectively employed to detect and characterize DNA adducts, paving the way for high-resolution, high-throughput profiling of DNA damage and the exposome.

bioinformatics↗

bioRxiv2025

Adaptive evolution of engineered Saccharomyces cerevisiae in favored and unusual chemical environments

Engineered microbial cells can produce a wide range of industrially relevant chemicals such as pharmaceuticals, fuels, and material precursors. The use of microbial cells for chemical production from renewable resources could replace oil-based chemistry and contribute to tackling global grand challenges of climate warming and resource insufficiency. However, it is underexplored how the chemical production by engineered microbial cells is affected by them being proliferating catalysts exposed to Darwinian selection. All proliferating cells are unavoidably subjected to Darwinian selection which favors fitness beneficial phenotypes that seldom include engineered chemical production. Here, adaptive laboratory evolution was performed to characterize the effect of Darwinian selection on Saccharomyces cerevisiae strains expressing two different heterologous pigment producing pathways, blue-coloured indigoidine and red-coloured bikaverin. S. cerevisiae haploid S288C based strain had the genes for bikaverin synthesis integrated in the same locus as the genes for indigoidine synthesis in haploid and diploid S. cerevisiae CEN.PK-based strains. The two different pigment producing strains were cultivated in rich and synthetic defined (without amino acids) media with respirative galactose as the sole carbon source for [~]200 and [~]175 generations, respectively. While CEN.PK-based lineages rapidly lost indigoidine pigmentation independent of growth medium or ploidy, bikaverin pigmentation in S288C-based lineages was robust. The adaptive solutions detected in S288C-based bikaverin producing lineages involved mutations in the galactose utilization pathway whereas the heterologous indigoidine pathway was recurrently mutated in the corresponding lineages. When the bikaverin producing S288C-based lineages were adaptively evolved on the favored glucose carbon source instead, pigmentation declined. Thus, the robustness of the engineered traits appears dependent on challenges in production environment and availability and fitness benefits of adaptive solutions. Whether or when engineered traits of microbial cells are robust when they proliferate in industrial use has scarcely been assessed. Here light was shed to the factors affecting the adaptive loss of engineered traits to facilitate the development of strains and biotechnological processes, including chemical environments, for robust long-term production.

synthetic biology↗

bioRxiv2024

New-to-nature PHA synthase design using deep learning

Polyhydroxyalkanaoate (PHA) synthases are a group of complex, dimeric enzymes which catalyse polymerization of Rhydroxyacids into PHAs. PHA properties depend on their monomer composition but enzymes found in nature have narrow specificities to certain R-hydroxyacids. In this study, a conditional variational autoencoder was used for the first time to design new-to-nature PHA synthases. The model was trained with natural protein sequences obtained from Uniprot and was used for the creation of approximately 10 000 new PHA synthase enzymes. Out of these, 16 sequences were selected for in vivo validation. The selection criteria included the presence of conserved residues such as catalytic amino acids and amino acids in the dimer interface and structural features like the number of -helixes in the N-terminal part of the enzyme. Two of the new-to-nature PHA synthases that had substantial numbers of amino acid substitutions (87 and 98) with respect to the most similar native enzymes were confirmed active and produced poly(hydroxybutyrate) (PHB) when expressed in yeast S. cerevisiae. PHA including PHB have high potential as biodegradable and biocompatible materials. Ultimately the model-designed new-to-nature PHA synthases, could expand the PHA material properties to suit new application areas.

bioengineering↗

bioRxiv2023

Reconstruction of compartmentalized genome-scalemetabolic models using deep learning for over 800fungi

Eukaryotic metabolism is organized into subcellular compartments enclosed by lipid-membranes. Since most metabolites do not move freely across the membranes, the compartmentalization influences metabolic pathway connectivities. Thus, compartmentalizing the pathways also in genome-scale metabolic models (GEMs) is essential for accurate predictions of eukaryotic cells metabolic phenotypes. Compartmentalization has manually been introduced into the model Eukaryote GEMs like for yeast Saccharomyces cerevisiae. Non-model organisms GEMs can be automatically reconstructed from genome data. However, the existing GEM reconstruction methods do not introduce compartmentalization into the models. To that end, we integrated our novel deep learning protein localization prediction and protein functional annotation into a top-down GEM reconstruction for automatically creating species-specific compartmentalized GEMs. We developed also a universal fungal GEM for top-down reconstruction of species-specifically compartmentalized GEMs and reconstructed models for 834 species. The novel protein localization prediction outperformed the state of the art in classifying proteins into multiple compartments and integrating the enzyme localization predictions to functional annotations improved the top-down model reconstruction. Interestingly, the clustering of the fungal GEMs reconstructed with predicted species-specific enzyme localization resembled more closely the phylogenetic relationships of the species than that of the GEMS without the compartmentalization. Compartmentalization of metabolism in fungi differs from other eukaryotes but the enzyme subcellular localizations vary also among fungal species. The Fungal kingdom encompasses species important for human health, environment, and industrial biotechnology. The reconstructed fungal GEM set offers valuable tools for e.g., predicting metabolic phenotypes of e.g., mushrooms or single-cellular fungi, the roles of eukaryotes in microbial communities, designs optimizing eukaryotic hosts for industrial chemical production, and identifying drug targets against pathogenic fungi. Beyond fungi, the compartmentalized GEM reconstruction method allows combining other universal GEMs for developing cell type specific GEMs for higher eukaryotes including plants, insects, and mammals.

systems biology↗

bioRxiv2023

Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts

BackgroundUse of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models. ResultsWe created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation. ConclusionsOur results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.

systems biology↗

bioRxiv2022

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes

Adaptive Laboratory Evolution (ALE) of microbes can improve the efficiency of sustainable industrial processes important to the global economy, but chance and genetic background effects often lead to suboptimal outcomes. Here we report an ALE platform to circumvent these flaws through parallelized clonal evolution at an unprecedented scale. Using this platform, we clonally evolved 104 yeast populations in parallel from many strains for eight desired wine production traits. Expansions of both ALE replicates and lineage numbers broadened the evolutionary search spectrum and increased the chances of evolving improved wine yeasts unencumbered by unwanted side effects. ALE gains often coincided with distinct aneuploidies and the emergence of semi-predictable side effects that were characteristic of each selection niche. Many high performing ALE strains retained their desired traits upon transfer to industrial conditions and produced high quality wine. Overall, our ALE platform brings evolutionary engineering into the realm of high throughput science and opens opportunities for rapidly optimizing microbes for use in many industrial sectors which otherwise could take many years to accomplish.

bioengineering↗

bioRxiv2022

Strain design optimization using reinforcement learning

Engineered microbial cells present a sustainable alternative to fossil-based synthesis of chemicals and fuels. Cellular synthesis routes are readily assembled and introduced into microbial strains using state-of-the-art synthetic biology tools. However, the optimization of the strains required to reach industrially feasible production levels is far less efficient. It typically relies on trial-and-error leading into high uncertainty in total duration and cost. New techniques that can cope with the complexity and limited mechanistic knowledge of the cellular regulation are called for guiding the strain optimization. In this paper, we put forward a multi-agent reinforcement learning (MARL) approach that learns from experiments to tune the metabolic enzyme levels so that the production is improved. Our method is model-free and does not assume prior knowledge of the microbes metabolic network or its regulation. The multi-agent approach is well-suited to make use of parallel experiments such as multi-well plates commonly used for screening microbial strains. We demonstrate the methods capabilities using the genome-scale kinetic model of Escherichia coli, k-ecoli457, as a surrogate for an in vivo cell behaviour in cultivation experiments. We investigate the methods performance relevant for practical applicability in strain engineering i.e. the speed of convergence towards the optimum response, noise tolerance, and the statistical stability of the solutions found. We further evaluate the proposed MARL approach in improving L-tryptophan production by yeast Saccharomyces cerevisiae, using publicly available experimental data on the performance of a combinatorial strain library. Overall, our results show that multi-agent reinforcement learning is a promising approach for guiding the strain optimization beyond mechanistic knowledge, with the goal of faster and more reliably obtaining industrially attractive production levels. Author summaryEngineered microbial cells offer a sustainable alternative solution to chemical production from fossil resources. However, to make the chemical production using microbial cells economically feasible, they need to be substantially optimized. Due to the biological complexity, this optimization to reach sufficiently high production is typically a costly trial and error process. This paper presents an Artificial Intelligence (AI) approach to guide this task. Our tool learns a model from previous experiments and uses the model to suggest improvements to the engineering design, until a satisfactory production performance is reached. This paper evaluates the behaviour of the proposed AI method from several angles, including the amount of experiments needed, the tolerance to noise as well as the stability of the proposed designs.

bioinformatics↗

bioRxiv2021

Predictive evolution of metabolic phenotypes using model-designed selection niches

Traits lacking fitness benefit cannot be directly selected for under Darwinian evolution. Thus, features such as metabolite secretion are currently inaccessible to adaptive laboratory evolution. Here, we utilize environment-dependency of trait correlations to enable Darwinian selection of fitness-neutral or costly traits. We use metabolic models to design selection niches and to identify surrogate traits that are genetically correlated with cell fitness in the selection niche but coupled to the desired trait in the target niche. Adaptive evolution in the selection niche and subsequent return to the target niche is thereby predicted to enhance the desired trait. We experimentally validate the theory by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds in wine fermentation. Genomic, transcriptomic, and proteomic changes in the evolved strains confirmed the predicted flux re-routing to aroma biosynthesis. The use of model-designed selection niches facilitates the predictive evolution of fitness-costly traits for ecological and biotechnological applications.

systems biology↗

bioRxiv2020

Operation of a TCA cycle subnetwork in the mammalian nucleus

Nucleic acid and histone modifications critically depend on central metabolism for substrates and co-factors. Although a few enzymes related to the formation of these required metabolites have been reported in the nucleus, the corresponding metabolic pathways are considered to function elsewhere in the cell. Here we show that a substantial part of the mitochondrial tricarboxylic acid (TCA) cycle, the biosynthetic hub of epigenetic modification factors, is operational also in the nucleus. Using 13C-tracer analysis, we identified activity of glutamine-to-fumarate, citrate-to-succinate, and glutamine-to-aspartate routes in the nuclei of HeLa cells. Proximity labeling mass-spectrometry revealed a spatial vicinity of the involved enzymes with core nuclear proteins, supporting their nuclear location. We further show nuclear localization of aconitase 2 and 2-oxoglutarate dehydrogenase in mouse embryonic stem cells. Together, our results demonstrate operation of an extended metabolic pathway in the nucleus warranting a revision of the canonical view on metabolic compartmentalization and gene expression regulation.

systems biology↗

bioRxiv2020

Production of biopolymer precursors beta-alanine and L-lactic acid from CO2 with metabolically versatile Rhodococcus opacus DSM 43205

Hydrogen oxidizing autotrophic bacteria are promising hosts for CO2 conversion into chemicals. In this work, we engineered the metabolically versatile lithoautotrophic bacterium Rhodococcus opacus strain DSM 43205 for synthesis of polymer precursors. Aspartate decarboxylase (panD) or lactate dehydrogenase (ldh) were expressed for beta-alanine or L-lactic acid production, respectively. The heterotrophic cultivations on glucose produced 25 mg L-1 beta-alanine and 742 mg L-1 L-lactic acid, while autotrophic cultivations with CO2, H2 and O2 resulted in the production of 1.8 mg L-1 beta-alanine and 146 mg L-1 L-lactic acid. Beta-alanine was also produced at 345 {micro}g L-1 from CO2 in electrobioreactors, where H2 and O2 were provided by water electrolysis. This work demonstrates that R. opacus DSM 43205 can be readily engineered to produce chemicals from CO2 and provides base for its further metabolic engineering.

bioengineering↗