External Validation of O-RADS US Risk Stratification and Management System.

Background The Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification and management system (O-RADS US) was designed to improve risk assessment and management of ovarian and adnexal lesions. Validation studies including both surgical and nonsurgical treatment as the reference standard remain lacking. Purpose To externally validate O-RADS US in women who underwent either surgical or nonsurgical treatment and to determine if incorporating acoustic shadowing as a benign finding improves diagnostic performance. Materials and Methods This retrospective study included consecutive women who underwent pelvic US between August 2015 and April 2017 at a tertiary referral oncology center. Two independent readers blinded to clinical and histologic outcome assigned an O-RADS risk category and an International Ovarian Tumor Analysis (IOTA) Assessment of Different NEoplasias in the adneXa (ADNEX) model risk of malignancy score to assessable lesions. Reference standards were surgical histopathology or 2-year imaging follow-up. Receiver operating characteristic (ROC) curve analysis was used to evaluate performance of the O-RADS US, ADNEX, and modified O-RADS models incorporating acoustic shadowing. Results In total, 227 women (mean age, 52 years ± 16 [SD]) with 262 ovarian or adnexal lesions were evaluated. Of these lesions, 187 (71%) were benign and 75 (29%) were malignant. The proportion of malignancy was 0% (0 of 100) for O-RADS 2, 3% (one of 32) for O-RADS 3, 35% (22 of 63) for O-RADS 4, and 78% (52 of 67) for O-RADS 5. The area under the ROC curve (AUC) for O-RADS and ADNEX was 0.91 (95% CI: 0.88, 0.94) and 0.95 (95% CI: 0.92, 0.97; P = .01), respectively. The addition of acoustic shadowing as a benign finding improved O-RADS AUC to 0.94 (95% CI: 0.91, 0.96; P = .01). Use of O-RADS 4 as a threshold yielded a sensitivity of 99% (74 of 75; 95% CI: 96, 100) and a specificity of 70% (131 of 187; 95% CI: 64, 77). Conclusion In a tertiary referral oncology center, the Ovarian-Adnexal Reporting and Data System US risk stratification and management system enabled accurate distinction of benign from malignant ovarian and adnexal lesions. Adding acoustic shadowing as a benign finding improved its diagnostic performance. © RSNA, 2022 See also the editorial by Levine in this issue.

Dimerization of SERCA2a Enhances Transport Rate and Improves Energetic Efficiency in Living Cells.

The type 2a sarco/endoplasmic reticulum (ER) Ca2+-ATPase (SERCA2a) plays a key role in intracellular Ca2+ regulation in the heart. We have previously shown evidence of stable homodimers of SERCA2a in heterologous cells and cardiomyocytes. However, the functional significance of the pump dimerization remains unclear. Here, we analyzed how SERCA2a dimerization affects ER Ca2+ transport. Fluorescence resonance energy transfer experiments in HEK293 cells transfected with fluorescently labeled SERCA2a revealed increasing dimerization of Ca2+ pumps with increasing expression level. This concentration-dependent dimerization provided means of comparison of the functional characteristics of monomeric and dimeric pumps. SERCA-mediated Ca2+ uptake was measured with the ER-targeted Ca2+ sensor R-CEPIA1er in cells cotransfected with SERCA2a and ryanodine receptor. For each individual cell, the maximal ER Ca2+ uptake rate and the maximal Ca2+ load, together with the pump expression level, were analyzed. This analysis revealed that the ER Ca2+ uptake rate increased as a function of SERCA2a expression, with a particularly steep, nonlinear increase at high expression levels. Interestingly, the maximal ER Ca2+ load also increased with an increase in the pump expression level, suggesting improved catalytic efficiency of the dimeric species. Reciprocally, thapsigargin inhibition of a fraction of the population of SERCA2a reduced not only the maximal ER Ca2+ uptake rate but also the maximal Ca2+ load. These data suggest that SERCA2a dimerization regulates Ca2+ transport by improving both the SERCA2a turnover rate and catalytic efficacy. Analysis of ER Ca2+ uptake in cells cotransfected with human wild-type SERCA2a (SERCA2aWT) and SERCA2a mutants with different catalytic activity revealed that an intact catalytic cycle in both protomers is required for enhancing the efficacy of Ca2+ transport by a dimer. The data are consistent with the hypothesis of functional coupling of two SERCA2a protomers in a dimer that reduces the energy barrier of rate-limiting steps of the catalytic cycle of Ca2+ transport.

Novel approach for quantification of endoplasmic reticulum Ca2+ transport.

The type 2a sarco-/endoplasmic reticulum Ca2+-ATPase (SERCA2a) plays a key role in Ca2+ regulation in the heart. However, available techniques to study SERCA function are either cell destructive or lack sensitivity. The goal of this study was to develop an approach to selectively measure SERCA2a function in the cellular environment. The genetically encoded Ca2+ sensor R-CEPIA1er was used to measure the concentration of Ca2+ in the lumen of the endoplasmic reticulum (ER) ([Ca2+]ER) in HEK293 cells expressing human SERCA2a. Coexpression of the ER Ca2+ release channel ryanodine receptor (RyR2) created a Ca2+ release/reuptake system that mimicked aspects of cardiac myocyte Ca2+ handling. SERCA2a function was quantified from the rate of [Ca2+]ER refilling after ER Ca2+ depletion; then, ER Ca2+ leak was measured after SERCA inhibition. ER Ca2+ uptake and leak were analyzed as a function of [Ca2+]ER to determine maximum ER Ca2+ uptake rate and maximum ER Ca2+ load. The sensitivity of this assay was validated by analyzing effects of SERCA inhibitors, [ATP]/[ADP], oxidative stress, phospholamban, and a loss-of-function SERCA2a mutation. In addition, the feasibility of using R-CEPIA1er to study SERCA2a in a native system was evaluated by using in vivo gene delivery to express R-CEPIA1er in mouse hearts. After ventricular myocyte isolation, the same methodology used in HEK293 cells was applied to study endogenous SERCA2a. In conclusion, this new approach can be used as a sensitive screening tool to study the effect of different drugs, posttranslational modifications, and mutations on SERCA function. NEW & NOTEWORTHY The aim of this study was to develop a sensitive approach to selectively measure sarco-/endoplasmic reticulum Ca2+-ATPase (SERCA) function in the cellular environment. The newly developed Ca2+ sensor R-CEPIA1er was used to successfully analyze Ca2+ uptake mediated by recombinant and native cardiac SERCA. These results demonstrate that this new approach can be used as a powerful tool to study new mechanisms of Ca2+ pump regulation.

MicroRNA-155 promotes autoimmune inflammation by enhancing inflammatory T cell development.

Mammalian noncoding microRNAs (miRNAs) are a class of gene regulators that have been linked to immune system function. Here, we have investigated the role of miR-155 during an autoimmune inflammatory disease. Consistent with a positive role for miR-155 in mediating inflammatory responses, Mir155(-/-) mice were highly resistant to experimental autoimmune encephalomyelitis (EAE). miR-155 functions in the hematopoietic compartment to promote the development of inflammatory T cells including the T helper 17 (Th17) cell and Th1 cell subsets. Furthermore, the major contribution of miR-155 to EAE was CD4(+) T cell intrinsic, whereas miR-155 was also required for optimum dendritic cell production of cytokines that promoted Th17 cell formation. Our study shows that one aspect of miR-155 function is the promotion of T cell-dependent tissue inflammation, suggesting that miR-155 might be a promising therapeutic target for the treatment of autoimmune disorders.

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants.

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.

The InterPro protein families database: the classification resource after 15 years.

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36,766 member database signatures integrated into 26,238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

InterPro in 2011: new developments in the family and domain prediction database.

InterPro (http://www.ebi.ac.uk/interpro/) is a database that integrates diverse information about protein families, domains and functional sites, and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in characterizing individual protein sequences. Herein we give an overview of new developments in the database and its associated software since 2009, including updates to database content, curation processes and Web and programmatic interfaces.

An affordable open-source turbidimeter.

Turbidity is an internationally recognized criterion for assessing drinking water quality, because the colloidal particles in turbid water may harbor pathogens, chemically reduce oxidizing disinfectants, and hinder attempts to disinfect water with ultraviolet radiation. A turbidimeter is an electronic/optical instrument that assesses turbidity by measuring the scattering of light passing through a water sample containing such colloidal particles. Commercial turbidimeters cost hundreds or thousands of dollars, putting them beyond the reach of low-resource communities around the world. An affordable open-source turbidimeter based on a single light-to-frequency sensor was designed and constructed, and evaluated against a portable commercial turbidimeter. The final product, which builds on extensive published research, is intended to catalyze further developments in affordable water and sanitation monitoring.

Impact of a Nutrition and Diabetes Continuing Education Program on Primary Care Provider's Knowledge, Attitude, and Clinical Practice.

Purpose: Diet/nutrition is the first-line non-pharmacological therapy in the treatment of diabetes. Diet/nutrition counseling is infrequently provided by primary care providers (PCPs), who have limited nutrition education in both medical and advanced practice provider curricula. This quality improvement project aimed to improve knowledge and attitude, and frequency of diet/nutrition counseling by PCPs among patients with uncontrolled diabetes (glycosylated hemoglobin A1c ≥8%), by providing an online continuing medical education (CME) program on diabetes diet/nutrition. Methods: At a community health center in New England, PCPs attended a live 50-minute online CME program that was developed internally, which taught PCPs current diet/nutrition guidelines and recommendations related to diabetes. Knowledge was assessed prior to and two weeks following the program, utilizing a 10-question tool, the Nutrition Management of Diabetes Assessment (NMDA). Similarly, attitude was evaluated using two subscales of the Nutrition in Patient Care Survey (NIPS): Nutrition in routine care and Physician efficacy. Chart audits of patients with uncontrolled diabetes were evaluated to assess PCPs documentation of diet/nutrition counseling four weeks prior to and six weeks following the CME program. Results: In September 2023, PCPs (n = 29) completed the CME program. There was an improvement in knowledge in the NMDA amongst PCPs (36% difference, p < 0.001). Attitude scores from two subscales of the NIPS were evaluated and both showed improvement, however only physician efficacy was significant (15% difference, p < 0.01). There was a positive difference in the trend of diet/nutrition counseling after the education program (p < 0.05). Conclusion: An online CME program improved diabetes diet/nutrition knowledge and attitude of PCPs and improved counseling practices for patients living with uncontrolled diabetes. The education program provided PCPs current standards of care guidelines/recommendations, which they can utilize when counseling patients with diabetes. PCPs would benefit from dedicated continuing education programs regarding diet/nutrition therapy for chronic illnesses.

Phosphorylation of phospholamban promotes SERCA2a activation by dwarf open reading frame (DWORF).

In cardiac myocytes, the type 2a sarco/endoplasmic reticulum Ca-ATPase (SERCA2a) plays a key role in intracellular Ca regulation. Due to its critical role in heart function, SERCA2a activity is tightly regulated by different mechanisms, including micropeptides. While phospholamban (PLB) is a well-known SERCA2a inhibitor, dwarf open reading frame (DWORF) is a recently identified SERCA2a activator. Since PLB phosphorylation is the most recognized mechanism of SERCA2a activation during adrenergic stress, we studied whether PLB phosphorylation also affects SERCA2a regulation by DWORF. By using confocal Ca imaging in a HEK293 expressing cell system, we analyzed the effect of the co-expression of PLB and DWORF using a bicistronic construct on SERCA2a-mediated Ca uptake. Under these conditions of matched expression of PLB and DWORF, we found that SERCA2a inhibition by non-phosphorylated PLB prevails over DWORF activating effect. However, when PLB is phosphorylated at PKA and CaMKII sites, not only PLB's inhibitory effect was relieved, but SERCA2a was effectively activated by DWORF. Förster resonance energy transfer (FRET) analysis between SERCA2a and DWORF showed that DWORF has a higher relative affinity for SERCA2a when PLB is phosphorylated. Thus, SERCA2a regulation by DWORF responds to the PLB phosphorylation status, suggesting that DWORF might contribute to SERCA2a activation during conditions of adrenergic stress.

High-quality sequence clustering guided by network topology and multiple alignment likelihood.

MOTIVATION: Proteins can be naturally classified into families of homologous sequences that derive from a common ancestor. The comparison of homologous sequences and the analysis of their phylogenetic relationships provide useful information regarding the function and evolution of genes. One important difficulty of clustering methods is to distinguish highly divergent homologous sequences from sequences that only share partial homology due to evolution by protein domain rearrangements. Existing clustering methods require parameters that have to be set a priori. Given the variability in the evolution pattern among proteins, these parameters cannot be optimal for all gene families. RESULTS: We propose a strategy that aims at clustering sequences homologous over their entire length, and that takes into account the pattern of substitution specific to each gene family. Sequences are first all compared with each other and clustered into pre-families, based on pairwise similarity criteria, with permissive parameters to optimize sensitivity. Pre-families are then divided into homogeneous clusters, based on the topology of the similarity network. Finally, clusters are progressively merged into families, for which we compute multiple alignments, and we use a model selection technique to find the optimal tradeoff between the number of families and multiple alignment likelihood. To evaluate this method, called HiFiX, we analyzed simulated sequences and manually curated datasets. These tests showed that HiFiX is the only method robust to both sequence divergence and domain rearrangements. HiFiX is fast enough to be used on very large datasets. AVAILABILITY AND IMPLEMENTATION: The Python software HiFiX is freely available at http://lbbe.univ-lyon1.fr/hifix.

On the identifiability of metabolic network models.

A major problem for the identification of metabolic network models is parameter identifiability, that is, the possibility to unambiguously infer the parameter values from the data. Identifiability problems may be due to the structure of the model, in particular implicit dependencies between the parameters, or to limitations in the quantity and quality of the available data. We address the detection and resolution of identifiability problems for a class of pseudo-linear models of metabolism, so-called linlog models. Linlog models have the advantage that parameter estimation reduces to linear or orthogonal regression, which facilitates the analysis of identifiability. We develop precise definitions of structural and practical identifiability, and clarify the fundamental relations between these concepts. In addition, we use singular value decomposition to detect identifiability problems and reduce the model to an identifiable approximation by a principal component analysis approach. The criterion is adapted to real data, which are frequently scarce, incomplete, and noisy. The test of the criterion on a model with simulated data shows that it is capable of correctly identifying the principal components of the data vector. The application to a state-of-the-art dataset on central carbon metabolism in Escherichia coli yields the surprising result that only 4 out of 31 reactions, and 37 out of 100 parameters, are identifiable. This underlines the practical importance of identifiability analysis and model reduction in the modeling of large-scale metabolic networks. Although our approach has been developed in the context of linlog models, it carries over to other pseudo-linear models, such as generalized mass-action (power-law) models. Moreover, it provides useful hints for the identifiability analysis of more general classes of nonlinear models of metabolism.

Pregnancy induces a fetal antigen-specific maternal T regulatory cell response that contributes to tolerance.

A fetus is inherently antigenic to its mother and yet is not rejected. The T regulatory (Treg) subset of CD4(+) T cells can limit immune responses and has been implicated in maternal tolerance of the fetus. Using virgin inbred mice undergoing a first syngenic pregnancy, in which only the male fetuses are antigenic, we demonstrate a maternal splenocyte proliferative response to the CD4(+) T cell restricted epitope of the male antigen (H-Y) in proportion to the fetal antigen load. A portion of the maternal immune response to fetal antigens is Treg in nature. The bystander suppressive function of pregnancy-generated Tregs requires the presence of the fetal antigen, demonstrating their inherent antigen specificity. In vivo targeting of diphtheria toxin to kill Tregs leads to a lower fraction of live male offspring and a selective reduction in mass of the surviving males. Thus, Tregs generated in the context of pregnancy function in an antigen-specific manner to limit the maternal immune response to the fetus in a successful pregnancy.

The relationship among gene expression, the evolution of gene dosage, and the rate of protein evolution.

The understanding of selective constraints affecting genes is a major issue in biology. It is well established that gene expression level is a major determinant of the rate of protein evolution, but the reasons for this relationship remain highly debated. Here we demonstrate that gene expression is also a major determinant of the evolution of gene dosage: the rate of gene losses after whole genome duplications in the Paramecium lineage is negatively correlated to the level of gene expression, and this relationship is not a byproduct of other factors known to affect the fate of gene duplicates. This indicates that changes in gene dosage are generally more deleterious for highly expressed genes. This rule also holds for other taxa: in yeast, we find a clear relationship between gene expression level and the fitness impact of reduction in gene dosage. To explain these observations, we propose a model based on the fact that the optimal expression level of a gene corresponds to a trade-off between the benefit and cost of its expression. This COSTEX model predicts that selective pressure against mutations changing gene expression level or affecting the encoded protein should on average be stronger in highly expressed genes and hence that both the frequency of gene loss and the rate of protein evolution should correlate negatively with gene expression. Thus, the COSTEX model provides a simple and common explanation for the general relationship observed between the level of gene expression and the different facets of gene evolution.

A case study of a unique advanced clinical skills elective at the David Geffen School of Medicine at UCLA.

Proficiency in clinical examination skills upon graduation from medical school is a core competency. Over the last few decades, the ability and confidence in this fundamental and crucial skill set has declined. The motivation and interest in meticulous physical examination by recently graduated residents has also eroded. In this case study, we describe a comprehensive, innovative, and immersive advanced clinical skills elective taken during the second half of the final year of medical school for students at the David Geffen School of Medicine. The course utilizes novel approaches to inspire, refresh and consolidate essential bedside observation skills and examination techniques. This approach gives senior students the confidence and fundamental understanding of how dedication to the patient exam can improve the doctor-patient relationship, core clinical reasoning and the practice of cost-effective and evidence-based care through their careers. We describe how the integration of fine art appreciation and introductory biding techniques are used to help students hone their visual diagnostic skills. We show how this is solidified through a longitudinal series of clinical image review sessions with diagnostic reasoning principles to formulate a clear differential. Point of care ultrasound, EKG analysis, advanced cardiac auscultation and diagnostic imaging skills are integrated in a comprehensive and memorable fashion. We present this case study to inspire clinical skills teachers everywhere to replicate our methods in resurrecting the importance of physical exams for their learners. Opening their trainees' eyes to new methods of honing their visual intelligence and developing healthy habits for stress and burnout reduction will aid the rest of their professional careers.

Cysteines 1078 and 2991 cross-linking plays a critical role in redox regulation of cardiac ryanodine receptor (RyR).

The most common cardiac pathologies, such as myocardial infarction and heart failure, are associated with oxidative stress. Oxidation of the cardiac ryanodine receptor (RyR2) Ca2+ channel causes spontaneous oscillations of intracellular Ca2+, resulting in contractile dysfunction and arrhythmias. RyR2 oxidation promotes the formation of disulfide bonds between two cysteines on neighboring RyR2 subunits, known as intersubunit cross-linking. However, the large number of cysteines in RyR2 has been a major hurdle in identifying the specific cysteines involved in this pathology-linked post-translational modification of the channel. Through mutagenesis of human RyR2 and in-cell Ca2+ imaging, we identify that only two cysteines (out of 89) in each RyR2 subunit are responsible for half of the channel's functional response to oxidative stress. Our results identify cysteines 1078 and 2991 as a redox-sensitive pair that forms an intersubunit disulfide bond between neighboring RyR2 subunits during oxidative stress, resulting in a pathological "leaky" RyR2 Ca2+ channel.

Identification of metabolic network models from incomplete high-throughput datasets.

MOTIVATION: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance. RESULTS: We develop a maximum-likelihood approach for the estimation of unknown parameters of metabolic network models that relies on the integration of statistical priors to compensate for the missing data. In the context of the linlog metabolic modeling framework, we implement the identification method by an Expectation-Maximization (EM) algorithm and by a simpler direct numerical optimization method. We evaluate performance of our methods by comparison to existing approaches, and show that our EM method provides the best results over a variety of simulated scenarios. We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature. This leads to promising results and allows us to highlight critical identification issues.

Accelerated removal of antibody-coated red blood cells from the circulation is accurately tracked by a biotin label.

BACKGROUND: Safe, accurate methods to reliably measure circulating red blood cell (RBC) kinetics are critical tools to investigate pathophysiology and therapy of anemia, including hemolytic anemias. This study documents the ability of a method using biotin-labeled RBCs (BioRBCs) to measure RBC survival (RCS) shortened by coating with a highly purified monomeric immunoglobulin G antibody to D antigen. STUDY DESIGN AND METHODS: Autologous RBCs from 10 healthy D+ subjects were labeled with either biotin or (51) Cr (reference method), coated (opsonized) either lightly (n = 4) or heavily (n = 6) with anti-D, and transfused. RCS was determined for BioRBCs and for (51) Cr independently as assessed by three variables: 1) posttransfusion recovery at 24 hours (PTR(24) ) for short-term RCS; 2) time to 50% decrease of the label (T(50) ), and 3) mean potential life span (MPL) for long-term RCS. RESULTS: BioRBCs tracked both normal and shortened RCS accurately relative to (51) Cr. For lightly coated RBCs, mean PTR(24) , T(50) , and MPL results were not different between BioRBCs and (51) Cr. For heavily coated RBCs, both short-term and long-term RCS were shortened by approximately 17 and 50%, respectively. Mean PTR(24) by BioRBCs (84 ± 18%) was not different from (51) Cr (81 ± 10%); mean T(50) by BioRBCs (23 ± 17 days) was not different from (51) Cr (22 ± 18 days). CONCLUSION: RCS shortened by coating with anti-D can be accurately measured by BioRBCs. We speculate that BioRBCs will be useful for studying RCS in conditions involving accelerated removal of RBCs including allo- and autoimmune hemolytic anemias.

Importance of metabolic coupling for the dynamics of gene expression following a diauxic shift in Escherichia coli.

Gene regulatory networks consist of direct interactions, but also include indirect interactions mediated by metabolism. We investigate to which extent these indirect interactions arising from metabolic coupling influence the dynamics of the system. To this end, we build a qualitative model of the gene regulatory network controlling carbon assimilation in Escherichia coli, and use this model to study the changes in gene expression following a diauxic shift from glucose to acetate. In particular, we compare the relative variation in the steady-state concentrations of enzymes and transcription regulators during growth on glucose and acetate, as well as the dynamic response of gene expression to the exhaustion of glucose and the subsequent assimilation of acetate. We find significant differences between the dynamics of the system in the absence and presence of metabolic coupling. This shows that interactions arising from metabolic coupling cannot be ignored when studying the dynamics of gene regulatory networks.