Biology subjects

Beck, A. E.

Publications and source records attributed to Beck, A. E..

7 recordsLinked to original sources

bioRxiv2026

Does specific membrane surface area constrain Escherichia coli energy spillage and therefore aerobic and anaerobic growth rates?

The physical dimensions of bacterial cells are finite, tightly regulated, and constrain phenotype based on cell surface area to volume ratios. Additionally, bacterial membranes have limited capacity to host proteins, constraining enzymatic potential per area. We combine these biophysical properties into an aggregate parameter termed specific membrane surface area (sMSA, nm2 gcdw-1) which is readily integrated into systems biology analyses including constraint-based stoichiometric models. sMSA-based analyses have remarkable agency to predict and interpret bacterial phenotypes including maximum growth rate, respiration efficiency, and potential for maintenance energy generation. The presented Escherichia coli research 1) quantitatively recreates the membrane proteome from central metabolism as a function of growth rate, 2) demonstrates that the maximum growth rate of both aerobic and anaerobic E. coli cultures abides by similar cell biology constraints including a Pareto tradeoff between simultaneous synthesis of biomass macromolecules and generation of maintenance energy, 3) quantifies the areal capacity of E. coli membranes to dissipate Gibbs energy during enthalpic and entropic dominated metabolisms, and 4) presents evidence that sMSA theory could permit isogenic populations to host multiple concurrent phenotypes with equivalent fitness metrics.The results support our sMSA theory, based on cell geometry and membrane enzyme capacity, as a substantial driver of metabolism in both oxic and anoxic environments.

systems biology↗

bioRxiv2026

Genome-scale community metabolic modeling of maize root-associated microbiota shows that root exudates stimulate diverse metabolic interactions

Microbes play a vital role in plant development, health, and resilience, yet relatively little is known about the specific metabolic mechanisms driving interactions in these host-associated communities. Systems biology models enable a computational approach to understanding metabolic interactions, which can be difficult to pinpoint experimentally; however, these methods cannot yet accommodate the large number of species in natural communities. Synthetic communities (SynComs) provide a more tractable alternative to explore targeted interactions. Here, we investigated metabolite exchange in a seven-member maize root-associated SynCom, specifically accounting for plant host context by designing a customized exudate medium. We constructed metabolic models for each bacterial species and curated them with in vitro phenotyping data to reflect experimentally based carbon uptake potential. Flux balance analysis of individual species demonstrated that integrating phenotype data and changing medium type had substantial impacts on predicted growth rates, which in turn shaped potential interspecies interactions. In silico community growth optimization of the seven-member community model showed that the exudate medium supported a more diverse community composition compared to minimal medium, with predictions of community member abundance closely aligned to literature-derived experimental results. Predicted metabolite exchange in the root exudate environment showed Enterobacter ludwigii as a community hub, and cross-feeding of indole suggested a potential effect of bacterial community interactions on the plant host. Our in silico findings indicate the host plays an important role in structuring microbial interactions and cross-feeding at the metabolic level, underscoring the importance of considering environmental context from both theoretical and experimental perspectives. IMPORTANCETrue understanding of a system is marked by the ability to predict its behavior. The complexity of natural host-microbe systems represents a frontier of knowledge that scientists are working to understand, and elucidating principles of interactions within multi-partite microbial communities remains a challenge in microbial ecology. Synthetic communities provide a tractable starting point for investigating interaction mechanisms, and computational approaches complement laboratory experiments by systematically evaluating multiple possibilities for metabolic pathway processing, thereby allowing us to comprehensively study the interconnected metabolic networks of host-associated microbiota. The model we developed for the seven-member maize root-associated bacterial community presents a step toward predicting plant-microbe behavior, providing hypotheses for future experimental testing and serving as a template for expanding model complexity to more members and other systems.

systems biology↗

bioRxiv2025

A Minimal Medium for Culturing Maize Root-Associated Microbes Based on a Plant Growth Medium

Plant-associated microbiota play a critical role in host resilience to both abiotic and biotic stresses. However, understanding the underlying mechanisms of interaction within these communities, as well as between these communities and their hosts, remains challenging due to the complexity and dynamic nature of plant-associated microbiota. Synthetic microbial communities (SynComs) can serve as experimentally tractable models to establish a fundamental understanding of plant-microbiota interactions. Here, we report the development of a defined, minimal growth medium for a well-characterized seven-member maize root SynCom. The medium is based on a standard plant-growth medium to enable its use for both in vitro and in planta studies. Using genome-scale metabolic modeling and auxotrophy prediction, we identified key nutrient requirements of each of the seven species and optimized the medium composition to support bacterial growth. This minimal medium enables controlled investigation of microbial physiology, metabolite exchange, and community interactions, and lays the foundation for scalable in vitro and in planta experiments, facilitating future research on plant microbiome functions.

microbiology↗

bioRxiv2025

Differential metaproteomics of bacteria grown in vitro and in planta reveals functions used during growth on maize roots

Microbes are ubiquitous in the rhizosphere and play crucial roles in plant health, yet the metabolisms and physiologies of individual species in planta remain poorly understood. In this study, we examined microbial gene expression in response to the maize root environment for seven bacterial species originally isolated from maize roots. We grew each species individually, both in vitro in a minimal medium and in planta, and used differential proteomics to identify functions upregulated specifically when bacteria are grown on maize roots. We identified between 1,500 and 2,100 proteins from each species, with 20-60% of these proteins being differentially abundant between the two conditions. While we found that transporter proteins were upregulated in all species in planta, all other differentially abundant functions varied greatly between species, suggesting niche specialization in root-associated microbes. Indeed, in vitro assays confirmed that Curtobacterium pusillum likely degrades plant hemicellulose, Enterobacter ludwigii may benefit the plant by phosphate solubilization, and Herbaspirillum robiniae colonizes maize roots more effectively when both of its Type VI Secretion Systems are functional. Together, our findings highlight both conserved and species-specific bacterial strategies for growth in the root environment and lay a foundation for future work investigating the mechanisms underlying plant-microbiota interactions. ImportanceBacteria that live on and around plant roots are important for plant growth and health, yet we still know relatively little about how individual bacterial species behave in this environment. In this study, we looked at seven bacterial species originally isolated from maize roots to understand how they change their metabolism and physiology when grown on the plant versus when grown under laboratory conditions. By doing this, we identified key strategies bacteria use to survive and thrive in the root environment, including changes in nutrient uptake, metabolism, and secretion systems. We also substantiated some of these behaviors using lab experiments and bacterial mutants. Understanding these species-specific functions helps us learn how bacteria establish themselves on roots and interact with the plant. This knowledge is critical for future efforts to design effective microbial communities that improve crop performance and resilience.

microbiology↗

bioRxiv2024

Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy.

The rules of prokaryotic cell design remain elusive. Here, a theory is presented for interpreting growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on cell dimensions, membrane protein crowding, and metabolism. The theory employs biophysical properties and systems analysis to successfully interpret phenotypes of Escherichia coli K-12 strains MG1655 and NCM3722. These strains are genetically similar but differ in surface area-to-volume (SA:V) ratios ([~]30%), growth rate on glucose ([~]40%), and overflow-inducing growth rates ([~]80%). Six predictions were tested and validated using experimental phenomics, proteomics, and mutant data. Analyses did not require assumptions regarding cytosolic macromolecular crowding highlighting the distinct properties of the theory. Cell geometry and membrane protein crowding are significant biophysical constraints of cell biology.

systems biology↗

bioRxiv2022

Environment constrains fitness advantages of division of labor in microbial consortia engineered for metabolite push or pull interactions

Fitness benefits from division of labor in microbial consortia are well documented, but the dependency of the benefits on environmental context is poorly understood. Two synthetic Escherichia coli consortia were built to test the relationships between exchanged organic acid, local environment, and opportunity costs of different metabolic strategies. Opportunity costs quantify benefits not realized due to selecting one phenotype over another. The consortia catabolized glucose and exchanged either acetic or lactic acid to create producer-consumer food webs. The organic acids had different inhibitory properties and different opportunity costs associated with their positions in central metabolism. The exchanged metabolites modulated different consortial dynamics. The acetic acid-exchanging (AAE) consortium had a push interaction motif where acetic acid was secreted faster by the producer than the consumer imported it, while the lactic acid-exchanging (LAE) consortium had a pull interaction motif where the consumer imported lactic acid at a comparable rate to its production. The LAE consortium outperformed wild type (WT) batch cultures under the environmental context of weakly buffered conditions, achieving a 55% increase in biomass titer, a 51% increase in biomass per proton yield, an 86% increase in substrate conversion, and the complete elimination of byproduct accumulation all relative to the WT. However, the LAE consortium had the tradeoff of a 42% lower specific growth rate. The AAE consortium did not outperform the WT in any considered fitness metric. Fitness advantages of the LAE consortium were sensitive to environment; increasing the medium buffering capacity negated the fitness advantages compared to WT. IMPORTANCEMost naturally occurring microorganisms persist in consortia where metabolic interactions are common and often essential to ecosystem function. This study uses synthetic ecology to test how different cellular interaction motifs influence fitness properties of consortia. Environmental context ultimately controlled the division of labor fitness as shifts from weakly buffered to highly buffered conditions negated the benefits of the strategy. Understanding the limits of division of labor advances our understanding of natural community functioning which is central to nutrient cycling and provides design rules for assembling consortia used in applied bioprocessing.

synthetic biology↗

bioRxiv2019

De novo EIF2AK1 and EIF2AK2 variants are associated with developmental delay, leukoencephalopathy, and neurologic decompensation

EIF2AK1 and EIF2AK2 encode members of the Eukaryotic Translation Initiation Factor 2 Alpha Kinase (EIF2AK) family that inhibits protein synthesis in response to physiologic stress conditions. EIF2AK2 is also involved in innate immune response and the regulation of signal transduction, apoptosis, cell proliferation, and differentiation. Despite these findings, human disorders associated with deleterious variants in EIF2AK1 and EIF2AK2 have not been reported. Here, we describe the identification of eight unrelated individuals with heterozygous de novo missense variants in EIF2AK1 (1/8) or EIF2AK2 (7/8). Features seen in these eight individuals include white matter alterations (8/8), developmental delay (8/8), impaired language (8/8), cognitive impairment (7/8), ataxia (6/8), dysarthria in probands with verbal ability (6/6), hypotonia (6/8), hypertonia (5/8), and involuntary movements (3/8). Individuals with EIF2AK2 variants also exhibit neurological regression in the setting of febrile illness or infection. We use mammalian cell lines and patient-derived fibroblasts to further confirm the pathogenicity of variants in these genes and found reduced kinase activity. EIF2AKs phosphorylate Eukaryotic Translation Initiation Factor 2 Subunit 1, (EIF2S1, also known as EIF2), which then inhibits EIF2B activity. Deleterious variants in genes encoding EIF2B proteins cause childhood ataxia with central nervous system hypomyelination/vanishing white matter disease (CACH/VWM), a leukoencephalopathy characterized by neurologic regression in the setting of febrile illness and other stressors. Our findings indicate that EIF2AK2 missense variants cause a neurodevelopmental syndrome that may share phenotypic and pathogenic mechanisms with CACH/VWM.

genetics↗