Biology subjects

Yurkovich, J. T.

Publications and source records attributed to Yurkovich, J. T..

15 recordsLinked to original sources

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↗

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

Gut Microbiome Pattern Reflects Healthy Aging and Predicts Extended Survival in Humans

The gut microbiome has important effects on human health, yet its importance in human aging remains unclear. Using two independent cohorts comprising 4582 individuals across the adult lifespan we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique with age. This uniqueness pattern is strongly associated with gut microbial amino acid derivatives circulating within the bloodstream, many of which have been previously identified as longevity biomarkers. At the latest stages of human life, two distinct patterns emerge wherein individuals in good health show continued microbial drift toward a unique compositional state, while the same drift is absent in individuals who perform worse on a number of validated health measures. The identified healthy aging pattern is characterized by an overall depletion of core genera found across most humans - primarily a depletion in the nearly ubiquitous genus Bacteroides. Consistently, retaining a high Bacteroides dominance into extreme age, or, equivalently, having a low gut microbiome uniqueness score, predicts decreased survival in a four-year follow-up. Our comprehensive analysis identifies the gut microbiome as a novel component of healthy aging, with important implications for the worlds growing older population.

systems biology↗

bioRxiv2019

The temperature dependence of platelet metabolism

Temperature plays a fundamental role in biology, influencing cellular function, affecting chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is not well understood but remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we have used time-course metabolomics data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than is glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperature indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways.\n\nStatement of SignificanceThe temperature dependence of cellular metabolism is difficult to study due to regulatory events that are activated upon deviation from the optimal temperature range. Platelets are blood components used in transfusion medicine but also serve as a model cell to study human energy metabolism in the absence of genetic regulation. We investigated changes in platelet metabolism at temperatures spanning from 4 {degrees}C-37 {degrees}C using a quantitative metabolic systems biology approach as opposed to assessing individual reactions. We found that energy producing metabolic pathways have different temperature sensitivities. The results define the metabolic response to temperature on the metabolic pathway level and are of importance for understanding the cryopreservation of human platelets and more complex human cells used in cellular therapy.

biochemistry↗

bioRxiv2019

Systems-level physiology of the human red blood cell is computed from metabolic and macromolecular mechanisms

The human red blood cell has served as a starting point for the application and development of systems biology approaches due to its simplicity, intrinsic experimental accessibility, and importance in human health applications. Here, we present a multi-scale computational model of the human red blood cell that accounts for the full metabolic network, key proteins (>95% of proteome mass fraction), and several macromolecular mechanisms. Proteomics data are used to place quantitative constraints on individual protein complexes that catalyze metabolic reactions, as well as a total proteome capacity constraint. We explicitly describe molecular mechanisms--such as hemoglobin binding and the formation and detoxification of reactive oxygen species--and takes standard hematological variables (e.g., hematocrit, hemoglobin concentration) as input, allowing for personalized physiological predictions. This model is built from first principles and allows for direct computation of physiologically meaningful quantities such as the oxygen dissociation curve and an accurate computation of the flux state of the metabolic network. More broadly, this work represents an important step toward including the proteome and its function in whole-cell models of human cells.

systems biology↗

bioRxiv2019

Identifying differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease using metabolic networks of human brain regions

Alzheimers disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in alternative bile acid synthesis pathway was expressed in brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

systems biology↗

bioRxiv2019

Intricate genetic programs controlling dormancy in Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB), responsible for the deadliest infectious disease worldwide, displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogens capacity to evade the immune system and opportunistically exploit immunocompromised individuals. Uncovering the gene regulatory programs that underlie the dramatic phenotypic shifts in MTB during disease latency and reactivation has posed an extraordinary challenge. We developed a novel experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the chain of transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genome-wide transcription factor binding location map and insights from network topology analysis, we identified regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions.\n\nOne Sentence SummaryHigh-resolution transcriptional time-course reveals six-state genetic program that enables MTB to enter and exit hypoxia-induced dormancy.

systems biology↗

bioRxiv2019

Community standards to facilitate development and address challenges in metabolic modeling

Standardization of data and models facilitates effective communication, especially in computational systems biology. However, both the development and consistent use of standards and resources remains challenging. As a result, the amount, quality, and format of the information contained within systems biology models are not consistent and therefore present challenges for widespread use and communication. Here, we focused on these standards, resources, and challenges in the field of metabolic modeling by conducting a community-wide survey. We used this feedback to (1) outline the major challenges that our field faces and to propose solutions and (2) identify a set of features that defines what a \"gold standard\" metabolic network reconstruction looks like concerning content, annotation, and simulation capabilities. We anticipate that this community-driven outline will help the long-term development of community-inspired resources as well as produce high-quality, accessible models. More broadly, we hope that these efforts can serve as blueprints for other computational modeling communities to ensure continued development of both practical, usable standards and reproducible, knowledge-rich models.

systems biology↗

bioRxiv2018

Visualizing metabolic network dynamics through time-series metabolomics data

New technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems--the human platelet and erythrocyte under cold storage for use in transfusion medicine.\n\nThe results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.\n\nIn conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.\n\nAUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

systems biology↗

bioRxiv2018

Systematic discovery of uncharacterized transcription factors in Escherichia coli K-12 MG1655

Transcriptional regulation enables cells to respond to environmental changes. Yet, among the estimated 304 candidate transcription factors (TFs) in Escherichia coli K-12 MG1655, 185 have been experimentally identified and only a few tens of them have been fully characterized by ChIP methods. Understanding the remaining TFs is key to improving our knowledge of the E. coli transcriptional regulatory network (TRN). Here, we developed an integrated workflow for the computational prediction and comprehensive experimental validation of TFs using a suite of genome-wide experiments. We applied this workflow to: 1) identify 16 candidate TFs from over a hundred candidate uncharacterized genes; 2) capture a total of 255 DNA binding peaks for 10 candidate TFs resulting in six high-confidence binding motifs; 3) reconstruct the regulons of these 10 TFs by determining gene expression changes upon deletion of each TF; and 4) determine the regulatory roles of three TFs (YiaJ, YdcI, and YeiE) as regulators of L-ascorbate utilization, proton transfer and acetate metabolism, and iron homeostasis under iron limited condition, respectively. Together, these results demonstrate how this workflow can be used to discover, characterize, and elucidate regulatory functions of uncharacterized TFs in parallel.

microbiology↗

bioRxiv2018

The Staphylococcus aureus Two-Component System AgrAC Displays Four Distinct Genomic Arrangements That Delineate Genomic Virulence Factor Signatures

Two-component systems (TCSs) consist of a histidine kinase and a response regulator. Here, we evaluated the conservation of the AgrAC TCS among 149 completely sequenced S. aureus strains. It is composed of four genes: agrBDCA. We found that: i) AgrAC system (agr) was found in all but one of the 149 strains; ii) The agr positive strains were further classified into four agr types based on AgrD protein sequences, iii) the four agr types not only specified the chromosomal arrangement of the agr genes but also the sequence divergence of AgrC histidine kinase protein, which confers signal specificity, iv) the sequence divergence was reflected in distinct structural properties especially in the transmembrane region and second extracellular binding domain, and v) there was a strong correlation between the agr type and the virulence genomic profile of the organism. Taken together, these results demonstrate that bioinformatic analysis of the agr locus leads to a classification system that correlates with the presence of virulence factors and protein structural properties.

microbiology↗

bioRxiv2017

Multi-scale model of the proteomic and metabolic consequences of reactive oxygen species

Catalysis using iron-sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can completely inhibit cell growth when unmanaged and thus elicits an essential stress response that is universal and fundamental in biology. We develop a computable multi-scale description of the ROS stress response in Escherichia coli. We show that this quantitative framework allows for the understanding and prediction of ROS stress responses at three levels: 1) pathways: amino acid auxotrophies, 2) networks: the systemic response to ROS stress, and 3) genetic basis: adaptation to ROS stress during laboratory evolution. These results show that we can now develop fundamental and quantitative genotype-phenotype relationships for stress responses on a genome-wide basis.

systems biology↗

bioRxiv2017

Catalytic potential and disturbance rejection of glycolytic kinases in the human red blood cell

The allosteric regulation of metabolic enzymes plays a key role in controlling the flux through metabolic pathways. The activity of such enzymes is traditionally described by allosteric rate laws in complex kinetic models of metabolic network function. As an alternative, we describe the fraction of the regulated enzyme that is in an active form by developing a detailed reaction network of all known ligand binding events to the enzyme. This fraction is the fundamental result of metabolic regulation as it represents the \"tug of war\" among the various regulators and substrates that determine the utilization of the enzyme. The active fraction corresponds to the utilization of the catalytic potential of the enzyme. Using well developed kinetic models of human red blood cell (RBC) glycolysis, we characterize the catalytic potential of its three key kinases: hexokinase (HEX), phosphofructokinase (PFK), and pyruvate kinase (PYK). We then compute their time-dependent interacting catalytic potentials. We show how detailed kinetic models of the management of the catalytic potential of the three kinases elucidates disturbance rejection capabilities of glycolysis. Further, we examine the sensitivity of the catalytic potential through an examination of existing personalized RBC models, providing a physiologically-meaningful sampling of the feasible parameter space. The graphical representation of the dynamic interactions of the individual kinase catalytic potential adjustment provides an easy way to understand how a robust homeostatic state is maintained through interacting allosteric regulatory mechanisms.

systems biology↗

bioRxiv2017

Systemic post-translational control of bacterial metabolism regulates adaptation in dynamic environments

Across all domains of life, elaborate control mechanisms regulate proteins, pathways, and cell phenotypes as organisms adapt to ever-changing environments. Post-translational modifications (PTMs) allow cells to rapidly and reversibly regulate molecular pathways, but it remains unclear how individual PTMs regulate fitness. Here, we studied >130 PTM sites in Escherichia coli to unravel how PTMs regulate cell metabolism and fitness in response to environmental changes, such as the glucose-acetate diauxie. Using a new metabolic modeling approach, we found a significant fraction of post-translationally modified enzymes are predicted to control shifts in pathway usage following evolutionarily-important environmental changes. Genetic screens using Multiplex Automated Genome Engineering confirmed that these PTMs impact cellular fitness, especially under dynamically changing environments. Finally, mechanisms of how individual PTMs impact protein function were detailed using molecular dynamics simulations and enzyme assays for enolase, transaldolase, and serine hydroxymethyltransferase. Thus, by integrating whole-cell data and pathway modeling with detailed biochemical analysis, we unraveled how individual PTMs regulate enzymes, pathways, and phenotypes to adapt to sudden environmental changes.

systems biology↗