Biology subjects

Zielinski, D. C.

Publications and source records attributed to Zielinski, D. C..

At least 19 recordsLinked to original sources

bioRxiv2025

Transcriptomic Profiling of scWAT Reveals Reduced Lipid-Associated Macrophage Signatures in Long-Term Endurance- and Resistance-Trained Athletes

Regular exercise training is associated with systemic and tissue-specific performance and health benefits, yet long-term molecular adaptations in subcutaneous abdominal white adipose tissue (scWAT) remain unclear. We analyzed the resting scWAT transcriptome of 89 adults (35-50 years) who were long-term (>15 years) endurance- or strength-trained, or untrained. Long-term training was linked to enrichment of ribosomal, mitochondrial, and aerobic metabolism-related pathways, while immune-related pathways were enriched in untrained individuals. A distinct sex dimorphic scWAT was identified: untrained females exhibited a more metabolically favorable, trained-like transcriptomic profile, and untrained males an untrained-like profile. In trained males, the inflammatory gene signature was lower than in untrained males. We also identified 11 genes specific to lipid-associated macrophages with lower expression in trained individuals, particularly in all endurance-trained individuals and strength-trained females. These findings highlight sex-specific, exercise-induced adaptations in scWAT relevant to metabolic health, and underscore the importance of including both sexes in scWAT research.

physiology↗

bioRxiv2025

Expanding the iModulon Knowledgebase for Bacillus subtilis: Updated Transcriptome Decomposition Reveals Novel Regulatory Mechanisms

1.The Gram-positive bacterium Bacillus subtilis is widely used as a model organism for studying cell differentiation, microbial physiology and gene regulation. Its genetic accessibility and strong protein secretion machinery make it an excellent host for industrial protein production. Transcriptomics has become an essential tool in understanding these traits and the organisms response to environmental stimuli. Recent advantages in transcriptomics have enabled genome-wide analysis of gene expression patterns under various environmental conditions and genetic perturbations, interpreting such data, however, remains challenging. Independent Component Analysis (ICA) has emerged as a powerful method to decompose bacterial transcriptomes into sets of co-regulated genes - so-called iModulons. In this study, we present an updated iModulon decomposition of Bacillus subtilis transcriptomes, generated from a compendium of 782 high-quality RNAseq samples. This dataset includes a wide range of environmental and genetic conditions. ICA decomposition revealed 142 iModulons, that captured 80% of the expression variance. We compare our version of the modularized B. subtilis transcriptome with previous decompositions and show that the updated composition enhances our understanding of B. subtilis gene regulation and enables novel insights into the organisms regulon structure and gene memberships of the WalR regulon.

systems biology↗

bioRxiv2024

Navigating the Fitness Landscapes of Plasmodium falciparum Dihydrofolate Reductase: Evolutionary Insights into Antifolate Resistance

The rapid emergence of drug resistance in malaria parasites poses a significant challenge to the efficacy of antifolate treatments. Traditional drug development approaches, which often rely on empirical screening with limited mechanistic insights, tend to overlook the complex evolutionary mechanisms that enable Plasmodium falciparum to evade drug inhibition while preserving enzyme functionality. In this study, we employed computational techniques to investigate the mutational landscape of dihydrofolate reductase (DHFR), focusing on regions essential for enzyme stability and resistance. Our analysis uncovered conserved residues essential for stability, mutation hotspots that enhance adaptability under drug pressure and co-evolving clusters revealing critical functional interdependencies. Through integrated approaches including mutational scanning, epistatic interaction modeling, and fitness trajectory mapping, we elucidated distinct evolutionary pathways that drive resistance. We were able to track the adaptive paths taken by wild-type residues upon mutation, revealing the steps required to reach high-fitness peaks within the rugged fitness landscape. These findings provide valuable insights into the molecular mechanisms of antifolate resistance. We suggest that future drug design should target co-evolving networks and conserved regions to support the development of next-generation therapies to overcome resistance.

biophysics↗

bioRxiv2024

Metabolic growth coupling strategies for in vivo enzyme selection systems

Whole-cell biocatalysis facilitates the production of a wide range of industrially and pharmaceutically relevant molecules from sustainable feedstocks such as plastic wastes, carbon dioxide, lignocellulose, or plant-based sugar sources. The identification and use of efficient enzymes in the applied biocatalyst is key to establishing economically feasible production processes. The generation and selection of favorable enzyme variants in adaptive laboratory evolution experiments using growth as a selection criterion is facilitated by tightly coupling enzyme catalytic activity to microbial metabolic activity. Here, we present a computational workflow to design strains that have a severe, growth-limiting metabolic chokepoint through a shared class of enzymes. The resulting chassis cell, termed enzyme selection system (ESS), is a platform for growth-coupling any enzyme from the respective enzyme class, thus offering cross-pathway application for enzyme engineering purposes. By applying the constraint-based modeling workflow, a publicly accessible database of 25,505 potential and experimentally tractable ESS designs was built for Escherichia coli and a broad range of production pathways with biotechnological relevance. Model-based analysis of the generated design database reveals a general design principle that the target enzyme activity is linked to overall microbial metabolic activity, not just the synthesis of one biomass precursor. Furthermore, the use of currently available genome-reduced strains or one preeminent carbon source does not significantly enable or improve growth-coupling of target enzymes. Most importantly, observed trade-offs between the predicted viability of ESSs, the design-inflicted metabolic perturbations, and the coupling strength suggest that a suboptimal coupling has benefits regarding the experimental implementation of ESSs and growth-coupling in general. Overall, the computed design database, which is accessible through https://biosustain.github.io/ESS-Designs/, and its analysis lay the foundation for generating valuable in vivo ESSs for a range of biotechnological applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/613188v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@15d5a10org.highwire.dtl.DTLVardef@1c4067aorg.highwire.dtl.DTLVardef@856ce9org.highwire.dtl.DTLVardef@1b14cdb_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG HighlightsO_LIEnzyme selection system designs link enzyme activities to global metabolic processes. C_LIO_LIEnzymes can be categorized based on growth-couplable reaction stoichiometries. C_LIO_LIA database of strain designs for building enzyme selection systems was created. C_LIO_LIEnzyme selection systems enable optimization of a range product synthesis pathways. C_LIO_LIApplication recommendations: weakly coupled enzyme activities can have practical advantages. C_LI

systems biology↗

bioRxiv2024

Deciphering the transcriptional regulatory network of Yarrowia lipolytica using machine learning

The transcriptional regulatory network (TRN) in Yarrowia lipolytica coordinates its cellular processes, including the response to various stimuli. The TRN has been difficult to study due to its complex nature. In industrial-size fermenters, environments are often not homogenous, resulting in Yarrowia experiencing fluctuating conditions during a fermentation. Compared with homogenous laboratory conditions, these fluctuations result in altered cellular states and behaviors due to the action of the TRN. Here, a machine learning approach was deployed to modularize the transcriptome to enable meaningful description of its changing composition. To provide a sufficiently broad dataset, a wide range of relevant fermentation conditions (nutrient limitations, growth rates, pH values, oxygen availability and CO2 stresses) were run and samples obtained for RNA-Seq generation. We thus significantly increased the number of publicly available transcriptomic dataset on Y. lipolytica W29. In total, 23 independently modulated gene sets (termed iModulons) were identified of which 9 could be linked to corresponding regulons in S. cerevisiae. Strong responses were found in relation to oxygen limitation and elevated CO2 concentrations represented by (i) altered ribosomal protein synthesis, (ii) cell cycle disturbances, (iii) respiratory gene expression, and (iv) redox homeostasis. These results provide a fine-grained systems-level understanding of the Y. lipolytica TRN in response to industrially meaningful stresses, providing engineering targets to design more robust production strains. Moreover, this study provides a guide to perform similar work with poorly characterized single-cellular eukaryotic organisms. Highlights- A large screening setup significantly expands the public RNA-Seq library on Y. lipolytica. - This work provides a systems-level understanding of the TRN in response to industrial stresses. - iModulon analysis can help accelerate bioprocess development. - Results can guide similar work on poorly characterized single-cellular eukaryotic organisms.

systems biology↗

bioRxiv2024

Data-driven modeling of E. coli transcriptional regulation

The growth of bacterial gene expression datasets has offered unprecedented coverage of achievable transcriptomes, reflecting diverse activity states of the transcription regulatory network. Machine learning methods like Independent Component Analysis (ICA) can decompose gene expression datasets into regulatory modules and condition-specific regulator activities. Here, we present a workflow to utilize inferred regulator activities to construct quantitative models of promoter regulation in E. coli. Resulting models are validated by predicting condition-specific TF effector concentrations and binding site motif strength based on differential gene expression data alone. We show how reconstructed promoter models can capture multi-scale regulation and disentangle regulator interactions, including resolving the apparent paradox where argR expression is positively correlated with its regulon despite being a repressor. We applied the workflow for all regulator-linked components extracted by ICA, demonstrating the scalability of the workflow to capture the E. coli TRN. This work suggests a path toward systematic, quantitative reconstruction of transcription regulatory networks driven by the large-scale databases that are now available for many organisms.

bioinformatics↗

bioRxiv2024

Extracellular Respiration is a Latent Energy Metabolism in Escherichia coli

Diverse microbial species utilize redox shuttles to exchange electrons with their environment through mediated extracellular electron transfer (EET). This process maintains redox homeostasis and supports anaerobic survival across diverse microbial communities. Although mediated EET has been extensively leveraged for bioelectrocatalysis and bioelectronics for decades, fundamental questions remain about how these redox shuttles are reduced within cells and their bioenergetic implications. This knowledge gap limits our understanding of the physiological roles of mediated EET in various microbes and hampers the development of efficient microbial electrochemical technologies. To address this, we developed a methodology integrating genome editing, electrochemistry, and systems biology to investigate the mechanism and bioenergetic implications of mediated EET in bacteria. Using this approach, we uncovered a mediated EET mechanism in Escherichia coli. In the absence of alternative electron sinks, the redox cycling of 2-hydroxy-1,4-naphthoquinone (HNQ) via a cytoplasmic nitroreductase enabled E. coli to respire and grow on an extracellular electrode. Genome-scale metabolic modeling suggested that HNQ- mediated EET offers a more energetically favorable route for supporting anaerobic growth than canonical fermentation. Transcriptome analysis revealed redox perturbations in response to HNQ and identified rapid metabolic adaptations that support growth. This work demonstrates that E. coli can grow independently of classical electron transport chains and fermentative pathways, unveiling a new type of anaerobic energy metabolism.

systems biology↗

bioRxiv2024

Trade-off Between Resistance and Persistence in High Cell Density Escherichia Coli Cultures

Microbes experience high cell density in many environments that come with diverse resource limitations and stresses. However, high density physiology remains poorly understood. We utilized well-controlled culturing systems to grow wild-type and metabolically engineered Escherichia coli strains into high cell densities (50-80 g Cdry cell weight L-1) and determine the associated transcriptional dynamics. Knowledge-enriched machine-learning-based analytics reveal distinct stress-related gene expression patterns that are consistent with a fundamental trade-off between resistance and persistence. We suggest that this trade-off explains observed growth arrests in high-density cultures and that it results from the disruption of cellular homeostasis, due to reallocation of limited cellular resources from resistance functions towards maintenance requirements of engineered production pathways. This study deepens our understanding of high-density physiology and demonstrates its importance to fundamental biomanufacturing challenges.

microbiology↗

bioRxiv2023

Reconstructing the Transcriptional Regulatory Network of Probiotic L. reuteri is Enabled by Transcriptomics and Machine Learning

ILimosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied independent component analysis to 117 high-quality RNA-seq datasets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. This study uncovers the fundamental properties of L. reuteris TRN, deepens our understanding of its arginine metabolism, and the co-regulation of riboflavin metabolism and fatty acid biosynthesis. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and the role of isoprenoid biosynthesis in L. reuteris adaptive response to environmental changes. Through the integration of transcriptomics and machine learning, we provide a systems-level understanding of L. reuteris response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/547516v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@118ec4corg.highwire.dtl.DTLVardef@141b0b7org.highwire.dtl.DTLVardef@1b8cf6forg.highwire.dtl.DTLVardef@3ab181_HPS_FORMAT_FIGEXP M_FIG C_FIG Comprehensive iModulon Workflow Overview. Our innovative workflow is grounded in the analysis of the LactoPRECISE compendium, a curated dataset containing 117 internally sequenced RNA-seq samples derived from a diversity of 50 unique conditions, encompassing an extensive range of 13 distinct condition types. We employ the power of Independent Component Analysis (ICA), a cutting-edge machine learning algorithm, to discern the underlying structure of iModulons within this wealth of data. In the subsequent stage of our workflow, the discovered iModulons undergo detailed scrutiny to uncover media-specific regulatory mechanisms governing metabolism, illuminate the context-dependent intricacies of gene expression, and predict pathways leading to the biosynthesis of probiotic secondary metabolites. Our workflow offers an invaluable and innovative lens through which to view probiotic strain design while simultaneously highlighting transformative approaches to data analytics in the field.

bioinformatics↗

bioRxiv2022

Elucidation of independently modulated genes in Streptococcus pyogenes reveals carbon sources that control its expression of hemolytic toxins

Streptococcus pyogenes can cause a wide variety of acute infections throughout the body of its human host. The underlying transcriptional regulatory network (TRN) is responsible for altering the physiological state of the bacterium to adapt to each host environment. Consequently, an in-depth understanding the comprehensive dynamics of its TRN could inform new therapeutic strategies. Here, we compiled 116 existing high-quality RNA-seq data sets of S. pyogenes serotype M1, and estimated the TRN structure in a top-down fashion by performing independent component analysis (ICA). The algorithm computed 42 independently modulated sets of genes (iModulons). Four iModulons contained nga-ifs-slo virulence-related operon, which allowed us to identify carbon sources that control its expression. In particular, dextrin utilization upregulated nga-ifs-slo operon by activation of two-component regulatory system CovRS-related iModulons, and changed bacterial hemolytic activity compared to glucose or maltose utilization. Finally, we show that the iModulon-based TRN structure can be used to simplify interpretation of noisy bacterial transcriptome at the infection site.

microbiology↗

bioRxiv2022

Quantitative sequence basis for the E. coli transcriptional regulatory network

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Inherently the DNA sequence is the primary determinant of the TRN; however, it is well established that the presence of a DNA binding motif does not guarantee a functional regulatory protein binding site. Thus, the extent to which the TRN architecture can be predicted by the genome DNA sequence alone remains unclear. Here, we developed machine learning models that predict the TRN structure of E. coli based on genome sequence. Models were constructed successfully (cross-validation AUROC >= 0.8) for 84% (57/68) of valid E. coli regulons identified from top-down analysis of RNA-seq data. We found that: 1) While regulatory motif strength is the most important sequence feature for determining regulon membership, additional features such as DNA shape substantially influence membership; 2) complex regulons involving multiple interacting regulators could be unraveled by machine learning; 3) investigating regulons where initial ML models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, while regulon structure can appear to be variable across estimation methods and strains, we found that strong regulatory sequence features underlie both the genes that appear most consistently in regulons across estimation methods as well as the core regulon genes in the Fur pan-regulon. This work develops a quantitative understanding of the sequence basis of the TRN and suggests a path towards computationally-guided control of transcriptional regulation for synthetic biology applications.

bioinformatics↗

bioRxiv2021

Data-Driven Strain Design Using Aggregated Adaptive Laboratory Evolution Mutational Data

Microbes are being engineered for an increasingly large and diverse set of applications. However, the designing of microbial genomes remains challenging due to the general complexity of biological system. Adaptive Laboratory Evolution (ALE) leverages natures problem-solving processes to generate optimized genotypes currently inaccessible to rational methods. The large amount of public ALE data now represents a new opportunity for data-driven strain design. This study presents a novel and first of its kind meta-analysis workflow to derive data-driven strain designs from aggregate ALE mutational data using rich mutation annotations, statistical and structural biology methods. The mutational dataset consolidated and utilized in this study contained 63 Escherichia coli K-12 MG1655 based ALE experiments, described by 93 unique environmental conditions, 357 independent evolutions, and 13,957 observed mutations. High-level trends across the entire dataset were established and revealed that ALE-derived strain designs will largely be gene-centric, as opposed to non-coding, and a relatively small number of variants (approx. 4) can significantly alter cellular states and provide benefits which range from an increase in fitness to a complete necessity for survival. Three novel experimentally validated designs relevant to metabolic engineering applications are presented as use cases for the workflow. Specifically, these designs increased growth rates with glycerol as a carbon source through a point mutation to glpK and a truncation to cyaA or increased tolerance to toxic levels of isobutyric acid through a pykF truncation. These results demonstrate how strain designs can be extracted from aggregated ALE data to enhance strain design efforts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/452699v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@8e1759org.highwire.dtl.DTLVardef@9ee7d5org.highwire.dtl.DTLVardef@866f04org.highwire.dtl.DTLVardef@1e27b29_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2021

A systems-guided approach to discover the intracellular target of a novel evolution-drug lead

Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we developed a systems-guided hierarchic workflow that utilizes metabolic and structural analysis to narrow in on specific targets suggested by statistical and machine learning analysis of metabolomics data. Utilizing a novel multi-valent DHFR-targeting antibiotic compound, CD15-3, as a case study, we first measured global metabolomics and applied statistics and machine learning to locate broad areas of metabolic perturbation under antibiotic stress. We then tested the ability of suggested compounds to rescue growth and performed metabolic modelling to identify pathways whose inhibition was consistent with growth rescue patterns. Next, we utilized protein structural similarity to further prioritize candidate drug targets within these pathways. Overexpression and in vitro activity assays of a top candidate target, HPPK (folK), showed complete recovery from drug induced growth inhibition and with microscopy. As interest in white-box machine learning methods continues to grow, this study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows.

systems biology↗

bioRxiv2020

The quantitative metabolome is shaped by abiotic constraints

Living systems formed and evolved under governing constraints that characterize their interactions with the inorganic world. These interactions are definable using basic physico-chemical principles. Here, we formulate a comprehensive set of ten governing abiotic constraints that define possible quantitative metabolomes. We apply these constraints to a metabolic network of Escherichia coli that represents 90% of its metabolome. We show that the quantitative metabolomes allowed by the abiotic constraints are consistent with metabolomic and isotope labeling data. We find that: (i) Network-wide characterization of charge-, proton- and magnesium-related constraints shape transcriptional regulatory responses to osmotic stress; (ii) Proton and charge imbalance underlie transcriptional regulatory responses to acid stress; (iii) Abiotic constraints drive the evolution of transport systems, such as high-affinity phosphate transporters. Thus, quantifying the constraints that the inorganic world imposes on living systems provides insights into their key characteristics, helps understand the outcomes of evolutionary adaptation, and should be considered as a fundamental part of theoretical biology and for understanding the constraints on evolution.

systems biology↗

bioRxiv2020

MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner; the Systems Biology Markup Language (SBML) simulation engine. Three case studies demonstrate how to use MASSpy: 1) to simulate dynamics of detailed mechanisms of enzyme regulation; 2) to generate an ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify uncertainty, and 3) to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenge that arise in dynamic modeling of metabolic networks, both at a small and large scale. Author SummaryGenome-scale reconstructions of metabolism appeared shortly after the first genome sequences became available. Constraint-based models are widely used to compute steady state properties of such reconstructions, but the attainment of dynamic models has remained elusive. We thus developed the MASSpy software package, a framework that enables the construction, simulation, and visualization of dynamic metabolic models. MASSpy is based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism. MASSpy seamlessly unites existing software packages within its framework to provide the user with various modeling tools in one package. MASSpy integrates community standards to facilitate the exchange of models, giving modelers the freedom to use the software for different aspects of their own modeling workflows. Furthermore, MASSpy contains methods for generating and simulating ensembles of models, and for explicitly accounting for biological uncertainty. MASSpy has already demonstrated success in a classroom setting. We anticipate that the suite of modeling tools incorporated into MASSpy will enhance the ability of the modeling community to construct and interrogate complex dynamic models of metabolism.

systems biology↗

bioRxiv2020

ReCodLiver0.9: Overcoming challenges in genome-scale metabolic reconstruction of a non-model species

The availability of genome sequences, annotations and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.Author summary Genome-scale metabolic models (GEMs) are constructed based upon reconstructed networks that are carried out by an organism. The underlying biochemical knowledge in such networks can be transformed into mathematical models that could serve as a platform to answer biological questions. The availability of high-throughput biological data, including genomics, proteomics, and metabolomics data, supports the generation of such models for a large number of organisms. Nevertheless, challenges arise for non-model species which are typically less annotated. In this paper, we discuss these challenges and possible solutions in the context of generation of a draft liver reconstruction of Atlantic cod (Gadus morhua). We also show how experimental data, here gene expression data, can be mapped to the resulting model to understand the metabolic response of cod liver to environmental toxicants.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2018

Modeling genome-wide evolution of catalytic turnover rates: Strong epistasis shaped modern enzyme kinetics

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. To address this question, we combine genome-scale metabolic modelling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. A small number of biophysically constrained enzymes suffice to induce diminishing returns epistasis that prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. In addition, multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape is smooth and causes kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data on in vitro and in vivo turnover rates, validating our modelling approach. Our analysis thus suggests that enzyme evolution can be predicted on a genome scale and reveals the mechanisms by which evolutionary forces shape modern kcats and the whole of cell metabolism.

evolutionary biology↗

bioRxiv2017

Functional pathways for metabolic network-based data analysis: the MetPath algorithm

Analyzing biological data using pathways helps identify trends in data tied to the function of a network. A large number of pathway-based analysis tools have been developed toward this goal. These pathways are often manually curated and thus associations are subject to the biases of the curator. A potentially attractive alternative is to define pathways based on the inherent functionality and connectivity of the network itself. Within metabolism, functionality is defined by the production and consumption of metabolites, and connectivity by metabolites participating in reactions through common enzymes. In this work, we present an algorithm, termed MetPath, that calculates pathways for production and consumption of metabolites. We show how these pathways have attractive properties, such as the ability to integrate multiple data types and weight contribution of genes within the pathway by their functional contribution to metabolite production/consumption. Pathways calculated in this manner are condition-specific and thus are custom tailored to the system of interest, in contrast to curated pathways. We find that these pathways predict gene expression correlation better compared to manually-curated pathways. Additionally, we find that these pathways can be used to understand gene expression changes between growth conditions and between cell types. This work serves to better understand the functional pathway structure underlying cell metabolism and helps to enable systems analyses of high-throughput data.

systems biology↗