Low affinity uniporter carrier proteins can increase net substrate uptake rate by reducing efflux

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Quantifying intracellular glucose levels when yeast is grown in glucose media

In Saccharomyces cerevisiae, intracellular glucose levels impact glucose transport and regulate carbon metabolism via various glucose sensors. To investigate mechanisms of glucose sensing, it is essential to know the intracellular glucose concentrations. Measuring intracellular glucose concentrations, however, is challenging when cells are grown on glucose, as glucose in the water phase around cells or stuck to the cell surface can be carried over during cell sampling and in the following attributed to intracellular glucose, resulting in an overestimation of intracellular glucose concentrations. Using lactose as a carryover marker in the growth medium, we found that glucose carryover originates from both the water phase and from sticking to the cell surface. Using a hexokinase null strain to estimate the glucose carryover from the cell surface, we found that glucose stuck on the cell surface only contributes a minor fraction of the carryover. To correct the glucose carryover, we revisited L-glucose as a carryover marker. Here, we found that L-glucose slowly enters cells. Thus, we added L-glucose to yeast cultures growing on uniformly 13C-labeled D-glucose only shortly before sampling. Using GC-MS to distinguish between the two differently labeled sugars and subtracting the carryover effect, we determined the intracellular glucose concentrations among two yeast strains with distinct kinetics of glucose transport to be at 0.89 mM in the wild-type strain and around 0.24 mM in a mutant with compromised glucose uptake. Together, our study provides insight into the origin of the glucose carryover effect and suggests that L-glucose added to the culture shortly before sampling is a possible method that yet has limitations with regard to measurement accuracy.

A New Mechanism of Carbon Metabolism and Acetic Acid Balance Regulated by CcpA

Catabolite control protein A (CcpA) is a critical regulator in Gram-positive bacteria that orchestrates carbon metabolism by coordinating the utilization of different carbon sources. Although it has been widely proved that CcpA helps prioritize the utilization of glucose over other carbon sources, this global regulator's precise mechanism of action remains unclear. In this study, a mutant Bacillus licheniformis deleted for CcpA was constructed. Cell growth, carbon utilization, metabolites and the transcription of key enzymes of the mutant strain were compared with that of the wild-type one. It was found that CcpA is involved in the regulation of glucose concentration metabolism in Bacillus. At the same time, CcpA regulates glucose metabolism by inhibiting acetic acid synthesis and pentose phosphate pathway key gene zwF. The conversion rate of acetic acid is increased by about 3.5 times after ccpA is deleted. The present study provides a new mechanism of carbon metabolism and acetic acid balance regulated by CcpA. On the one hand, this work deepens the understanding of the regulatory function of CcpA and provides a new view on the regulation of glucose metabolism. On the other hand, it is helpful to the transformation of B. licheniformis chassis microorganisms.

Structure and mechanism of oxalate transporter OxlT in an oxalate-degrading bacterium in the gut microbiota

An oxalate-degrading bacterium in the gut microbiota absorbs food-derived oxalate to use this as a carbon and energy source, thereby reducing the risk of kidney stone formation in host animals. The bacterial oxalate transporter OxlT selectively uptakes oxalate from the gut to bacterial cells with a strict discrimination from other nutrient carboxylates. Here, we present crystal structures of oxalate-bound and ligand-free OxlT in two distinct conformations, occluded and outward-facing states. The ligand-binding pocket contains basic residues that form salt bridges with oxalate while preventing the conformational switch to the occluded state without an acidic substrate. The occluded pocket can accommodate oxalate but not larger dicarboxylates, such as metabolic intermediates. The permeation pathways from the pocket are completely blocked by extensive interdomain interactions, which can be opened solely by a flip of a single side chain neighbouring the substrate. This study shows the structural basis underlying metabolic interactions enabling favourable symbiosis.

Elucidating yeast glycolytic dynamics at steady state growth and glucose pulses through kinetic metabolic modeling

Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.

Mechanistic aspects of IPTG (isopropylthio-β-galactoside) transport across the cytoplasmic membrane of Escherichia coli-a rate limiting step in the induction of recombinant protein expression

Coupling transcription of a cloned gene to the lac operon with induction by isopropylthio-β-galactoside (IPTG) has been a favoured approach for recombinant protein expression using Escherichia coli as a heterologous host for more than six decades. Despite a wealth of experimental data gleaned over this period, a quantitative relationship between extracellular IPTG concentration and consequent levels of recombinant protein expression remains surprisingly elusive across a broad spectrum of experimental conditions. This is because gene expression under lac operon regulation is tightly correlated with intracellular IPTG concentration due to allosteric regulation of the lac repressor protein (lacY). An in-silico mathematical model established that uptake of IPTG across the cytoplasmic membrane of E. coli by simple diffusion was negligible. Conversely, lacY mediated active transport was a rapid process, taking only some seconds for internal and external IPTG concentrations to equalize. Optimizing kcat and KM parameters by targeted mutation of the galactoside binding site in lacY could be a future strategy to improve the performance of recombinant protein expression. For example, if kcat were reduced whilst KM was increased, active transport of IPTG across the cytoplasmic membrane would be reduced, thereby lessening the metabolic burden on the cell and expediating accumulation of recombinant protein. The computational model described herein is made freely available and is amenable to optimize recombinant protein expression in other heterologous hosts.

ONE-SENTENCE SUMMARY

A computational model made freely available to optimize recombinant protein expression in Escherichia coli other heterologous hosts.

Perfect adaptation achieved by transport limitations governs the inorganic phosphate response in S. cerevisiae

Cells cope with and adapt to ever-changing environmental conditions. Sophisticated regulatory networks allow cells to adjust to these fluctuating environments. One such archetypal system is the Saccharomyces cerevisiae Pho regulon. When external inorganic phosphate (P_i) concentration is low, the Pho regulon activates, expressing genes that scavenge external and internal P_i. However, the precise mechanism controlling this regulon remains elusive. We conducted a systems analysis of the Pho regulon on the single-cell level under well-controlled environmental conditions. This analysis identified a robust, perfectly adapted Pho regulon state in intermediate P_i conditions, and we identified an intermediate nuclear localization state of the transcriptional master regulator Pho4p. The existence of an intermediate nuclear Pho4p state unifies and resolves outstanding incongruities associated with the Pho regulon, explains the observed programmatic states of the Pho regulon, and improves our general understanding of how nature evolves and controls sophisticated gene regulatory networks. We further propose that robustness and perfect adaptation are not achieved through complex network-centric control but by simple transport biophysics. The ubiquity of multitransporter systems suggests that similar mechanisms could govern the function of other regulatory networks as well.

Using Kinetic Modelling to Infer Adaptations in Saccharomyces cerevisiae Carbohydrate Storage Metabolism to Dynamic Substrate Conditions

Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, ¹³C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.

Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints

Eukaryotic cells are used as cell factories to produce and secrete multitudes of recombinant pharmaceutical proteins, including several of the current top-selling drugs. Due to the essential role and complexity of the secretory pathway, improvement for recombinant protein production through metabolic engineering has traditionally been relatively ad-hoc; and a more systematic approach is required to generate novel design principles. Here, we present the proteome-constrained genome-scale protein secretory model of yeast Saccharomyces cerevisiae (pcSecYeast), which enables us to simulate and explain phenotypes caused by limited secretory capacity. We further apply the pcSecYeast model to predict overexpression targets for the production of several recombinant proteins. We experimentally validate many of the predicted targets for α-amylase production to demonstrate pcSecYeast application as a computational tool in guiding yeast engineering and improving recombinant protein production.

Due to the complexity of the protein secretory pathway, strategy suitable for the production of a certain recombination protein cannot be generalized. Here, the authors construct a proteome-constrained genome-scale protein secretory model for yeast and show its application in the production of different misfolded or recombinant proteins.

Influence of the Zeolite ZSM-22 Precursor on a UF-PES Selective Substrate Layer for Salts Rejection

Fabrication of the ZSM-22/Polyethersulfone (ZSM-22/PES) membranes as selective salt filters represent a growing membrane technological area in separation with the potential of high economic reward based on its low energy requirements. The incorporation of ZSM-22 zeolite material as additives into the PES polymer matrix has the prospective advantage of combining both the zeolite and polymer features while overcoming the limitations associated with both materials. This work investigated the influence of the nature of the silica precursor on ZSM-22 zeolite hydrothermally synthesised using colloidal (C60) and fumed (C60) silica to Si/Al of 60. The successful synthesis of the highly crystalline zeolitic materials was confirmed through XRD, FTIR, and SEM with EDX. The ZSM-22 additives were directly dispersed into a PES polymeric matrix to form a casting solution for the preparation of the ZSM-22/PES selective substrate layers via a phase inversion method for salts rejection. The polymeric PES was selected as an organic network in which the content of the ZSM-22 zeolite (ranging between 0 and 1.0 wt.%), was obtained and characterised by XRD, FTIR, and SEM analysis, as well as water contact angle (WCA) measurement and dead-end filtration cell. The phase inversion preparation method has induced the resulting ZSM-22/PES NF substrates anisotropy, as attributed to a high water flux to the above 700 L·m−2·h−1; high selectivity and rejection of salts to above 80% is revealed by the obtained results. The materials also exhibited improved antifouling behavior to above 70% flux recovery ratios. As such, the nature of the silica precursor influences ZSM-22 zeolite synthesis as a potential additive in the PES polymer matrix and led to the enhanced performance of the pure PES ultrafiltration membrane.

Multiple nutrient transporters enable cells to mitigate a rate-affinity tradeoff

Eukaryotic genomes often encode multiple transporters for the same nutrient. For example, budding yeast has 17 hexose transporters (HXTs), all of which potentially transport glucose. Using mathematical modelling, we show that transporters that use either facilitated diffusion or symport can have a rate-affinity tradeoff, where an increase in the maximal rate of transport decreases the transporter’s apparent affinity. These changes affect the import flux non-monotonically, and for a given concentration of extracellular nutrient there is one transporter, characterised by its affinity, that has a higher import flux than any other. Through encoding multiple transporters, cells can therefore mitigate the tradeoff by expressing those transporters with higher affinities in lower concentrations of nutrients. We verify our predictions using fluorescent tagging of seven HXT genes in budding yeast and follow their expression over time in batch culture. Using the known affinities of the corresponding transporters, we show that their regulation in glucose is broadly consistent with a rate-affinity tradeoff: as glucose falls, the levels of the different transporters peak in an order that mostly follows their affinity for glucose. More generally, evolution is constrained by tradeoffs. Our findings indicate that one such tradeoff often occurs in the cellular transport of nutrients.

From yeast to humans, cells often express multiple different types of transporters for the same nutrient, and it is puzzling why a single high-affinity transporter is not expressed instead. Here we initially use mathematical modelling to demonstrate that transporters facilitating diffusion and those powered by the proton motive force can both exhibit a rate-affinity tradeoff, for quite general conditions. A transporter with a higher affinity necessarily has a lower rate, and vice versa. The tradeoff implies that there is a range of nutrient concentrations for which a transporter, characterised by its affinity, has a higher import flux than any other transporter with a different affinity. To mitigate the tradeoff, genomes may therefore encode multiple different transporters, and cells that express each transporter in the concentrations where it imports best will uptake nutrients at higher rates. Consistently, we show that as cells of budding yeast consume glucose, they express five types of hexose transporters in an order that follows the transporters’ affinities.

Functional characterization of Atlantic salmon (Salmo salar L.) PepT2 transporters

Abstract

The high‐affinity/low‐capacity system Slc15a2 (PepT2) is responsible for the reuptake of di/tripeptides from the renal proximal tubule, but it also operates in many other tissues and organs. Information regarding PepT2 in teleost fish is limited and, to date, functional data are available from the zebrafish (Danio rerio) only. Here, we report the identification of two slc15a2 genes in the Atlantic salmon (Salmo salar) genome, namely slc15a2a and slc15a2b. The two encoded PepT2 proteins share 87% identity and resemble both structurally and functionally the canonical vertebrate PepT2 system. The mRNA tissue distribution analyses reveal a widespread distribution of slc15a2a transcripts, being more abundant in the brain and gills, while slc15a2b transcripts are mainly expressed in the kidney and the distal part of the gastrointestinal tract. The function of the two transporters was investigated by heterologous expression in Xenopus laevis oocytes and two‐electrode voltage‐clamp recordings of transport and presteady‐state currents. Both PepT2a and PepT2b in the presence of Gly‐Gln elicit pH‐dependent and Na⁺ independent inward currents. The biophysical and kinetic analysis of the recorded currents defined the transport properties, confirming that the two Atlantic salmon PepT2 proteins behave as high‐affinity/low‐capacity transporters. The recent structures and the previous kinetic schemes of rat and human PepT2 qualitatively account for the characteristics of the two Atlantic salmon proteins. This study is the first to report on the functional expression of two PepT2‐type transporters that operate in the same vertebrate organism as a result of (a) gene duplication process(es).

Key points

Two slc15a2‐type genes, slc15a2a and slc15a2b coding for PepT2‐type peptide transporters were found in the Atlantic salmon.

slc15a2a transcripts, widely distributed in the fish tissues, are abundant in the brain and gills, while slc15a2b transcripts are mainly expressed in the kidney and distal gastrointestinal tract.

Amino acids involved in vertebrate Slc15 transport function are conserved in PepT2a and PepT2b proteins.

Detailed kinetic analysis indicates that both PepT2a and PepT2b operate as high‐affinity transporters.

The kinetic schemes and structures proposed for the mammalian models of PepT2 are suitable to explain the function of the two Atlantic salmon transporters.

Enzyme Trafficking and Co-Clustering Precede and Accurately Predict Human Breast Cancer Recurrences: An Interdisciplinary Review

Although great effort has been expended to understand cancer's origins, less attention has been given to the primary cause of cancer deaths - cancer recurrences and their sequelae. This interdisciplinary review addresses mechanistic features of aggressive cancer by studying metabolic enzyme patterns within ductal carcinoma in situ (DCIS) of the breast lesions. DCIS lesions from patients who did or did not experience a breast cancer recurrence were compared. Several proteins, including phospho-Ser226-glucose transporter type 1, phosphofructokinase type L and phosphofructokinase/fructose 2,6-bisphosphatase type 4 are found in nucleoli of ductal epithelial cells in samples from patients who will not subsequently recur, but traffic to the cell periphery in samples from patients who will experience a cancer recurrence. Large co-clusters of enzymes near plasmalemmata will enhance product formation because enzyme concentrations in clusters are very high while solvent molecules and solutes diffuse through small channels. These structural changes will accelerate aerobic glycolysis. Agglomerations of pentose phosphate pathway and glutathione synthesis enzymes enhance GSH formation. As aggressive cancer lesions are incomplete at early stages, they may be unrecognizable. We have found that machine learning provides superior analyses of tissue images and may be used to identify biomarker patterns associated with recurrent and non-recurrent patients with high accuracy. This suggests a new prognostic test to predict DCIS patients who are likely to recur and those who are at low risk for recurrence. Mechanistic interpretations provide a deeper understanding of anti-cancer drug action and suggest that aggressive metastatic cancer cells are sensitive to reductive chemotherapy.

Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli

Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L−1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.

Development and quantitative analysis of a biosensor based on the Arabidopsis SWEET1 sugar transporter

Transporters are the gatekeepers of the cell. Transporters facilitate the exchange of ions and metabolites between cellular and subcellular compartments, thus controlling processes from bacterial chemotaxis to the release of neurotransmitters. In plants, transporters have key roles in the allocation of carbon to nonphotosynthetic organs. Biosensors derived from transporters have been generated to monitor the activity of these proteins within the complex environment of the cell. However, a quantitative framework that reconciles molecular and cellular-level events to help interpret the response of biosensors is still lacking. Here, we created a sugar transporter biosensor and formulated a mathematical model to explain its response. These types of models can help realize multiscale, dynamic simulations of metabolite allocation to guide crop improvement.

SWEETs are transporters with homologs in Archeae, plants, some fungi, and animals. As the only transporters known to facilitate the cellular release of sugars in plants, SWEETs play critical roles in the allocation of sugars from photosynthetic leaves to storage tissues in seeds, fruits, and tubers. Here, we report the design and use of genetically encoded biosensors to measure the activity of SWEETs. We created a SweetTrac1 sensor by inserting a circularly permutated green fluorescent protein into the Arabidopsis SWEET1, resulting in a chimera that translates substrate binding during the transport cycle into detectable changes in fluorescence intensity. We demonstrate that a combination of cell sorting and bioinformatics can accelerate the design of biosensors and formulate a mass action kinetics model to correlate the fluorescence response of SweetTrac1 with the transport of glucose. Our analysis suggests that SWEETs are low-affinity, symmetric transporters that can rapidly equilibrate intra- and extracellular concentrations of sugars. This approach can be extended to SWEET homologs and other transporters.

Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes

Escherichia coli is an important cause of bacterial infections worldwide, with multidrug-resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in health care settings, the presence of subinhibitory antimicrobial residues in the environment and in clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. Here, we used machine learning to predict the population doubling time and cell growth yield of 1,407 genetically diverse E. coli strains expanding under exposure to three subinhibitory concentrations of six classes of antimicrobials from single-nucleotide genetic variants, accessory gene variation, and the presence of known AMR genes. We predicted cell growth yields in the held-out test data with an average correlation (Spearman's ρ) of 0.63 (0.36 to 0.81 across concentrations) and cell doubling times with an average correlation of 0.59 (0.32 to 0.92 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This finding points to the remaining missing heritability of growth under antimicrobial exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual single-nucleotide polymorphisms (SNPs) and genes. Predictions based on whole-genome information were generally superior to those based only on known AMR genes and were accurate for AMR resistance at therapeutic concentrations. We pinpointed genes and SNPs determining the predicted growth and thereby recapitulated many known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing the growth effects for known resistance genes in each individual strain. Our results underscore the potential of predictive modeling of growth patterns from genomic data under subinhibitory concentrations of antimicrobials, although the remaining missing heritability poses a challenge for achieving the accuracy and precision required for clinical use. IMPORTANCE Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains. In this study, we used a high-throughput phenotypic assay to measure the growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under nontherapeutic subinhibitory concentrations of antimicrobials that are common in nonclinical settings. We found a moderate to strong correlation between predicted and actual values for the different collected data sets. Moreover, we observed that the known resistance genes are still effective at sublethal concentrations, pointing to clinical implications of these concentrations.

Relocation of phosphofructokinases within epithelial cells is a novel event preceding breast cancer recurrence that accurately predicts patient outcomes

Although recurrent cancers are often aggressive, little is known about the intracellular events required for cancer recurrences. Due to this lack of mechanistic information, there is no test to predict cancer recurrences or non-recurrences during early stages of disease. In this retrospective study, we use ductal carcinoma in situ (DCIS) of the breast as a framework to better understand the mechanism of cancer recurrences using patient outcomes as the physiological observable. Conventional pathology slides were labeled with anti-phosphofructokinase type L (PFKL) and anti-phosphofructokinase/fructose-2,6-bisphosphatase type 4 (PFKFB4) reagents. PFKL and PFKFB4 were found in ductal epithelial cell nucleoli from DCIS samples of women who did not experience a cancer recurrence. In contrast, PFKL and PFKFB4 may be found near the plasma membrane in samples from patients who will develop recurrent cancer. Using machine learning to predict patient outcomes, holdout studies of individual patient micrographs for the three biomarkers PFKL, PFKFB4, and phosphorylated GLUT1 demonstrated 38.6% true negatives, 49.5% true positives, 11.9% false positives and 0% false negatives (N=101). A sub-population of recurrent samples demonstrated PFKL, PFKFB4, and phosphorylated glucose transporter 1 accumulation at the apical surface of epithelial cells, suggesting that carbohydrates can be harvested from the ducts' luminal spaces as an energy source. We suggest that PFK isotype patterns are metabolic switches representing key mechanistic steps of recurrences. Furthermore, PFK enzyme patterns within epithelial cells contribute to an accurate diagnostic test to classify DCIS patients as high or low recurrence risk.

Molecular Insight into the Regulation of Vimentin by Cysteine Modifications and Zinc Binding

The intermediate filament protein vimentin is involved in essential cellular processes, including cell division and stress responses, as well as in the pathophysiology of cancer, pathogen infection, and autoimmunity. The vimentin network undergoes marked reorganizations in response to oxidative stress, in which modifications of vimentin single cysteine residue, Cys328, play an important role, and is modulated by zinc availability. However, the molecular basis for this regulation is not fully understood. Here, we show that Cys328 displays a low pK_a, supporting its reactivity, and is readily alkylated and oxidized in vitro. Moreover, combined oxidation and crosslinking assays and molecular dynamics simulations support that zinc ions interact with Cys328 in its thiolate form, whereas Glu329 and Asp331 stabilize zinc coordination. Vimentin oxidation can induce disulfide crosslinking, implying the close proximity of Cys328 from neighboring dimers in certain vimentin conformations, supported by our computational models. Notably, micromolar zinc concentrations prevent Cys328 alkylation, lipoxidation, and disulfide formation. Moreover, zinc selectively protects vimentin from crosslinking using short-spacer cysteine-reactive but not amine-reactive agents. These effects are not mimicked by magnesium, consistent with a lower number of magnesium ions hosted at the cysteine region, according to molecular dynamics simulations. Importantly, the region surrounding Cys328 is involved in interaction with several drugs targeting vimentin and is conserved in type III intermediate filaments, which include glial fibrillary acidic protein and desmin. Altogether, our results identify this region as a hot spot for zinc binding, which modulates Cys328 reactivity. Moreover, they provide a molecular standpoint for vimentin regulation through the interplay between cysteine modifications and zinc availability.

Mechanistic model of nutrient uptake explains dichotomy between marine oligotrophic and copiotrophic bacteria

Marine bacterial diversity is immense and believed to be driven in part by trade-offs in metabolic strategies. Here we consider heterotrophs that rely on organic carbon as an energy source and present a molecular-level model of cell metabolism that explains the dichotomy between copiotrophs—which dominate in carbon-rich environments—and oligotrophs—which dominate in carbon-poor environments—as the consequence of trade-offs between nutrient transport systems. While prototypical copiotrophs, like Vibrios, possess numerous phosphotransferase systems (PTS), prototypical oligotrophs, such as SAR11, lack PTS and rely on ATP-binding cassette (ABC) transporters, which use binding proteins. We develop models of both transport systems and use them in proteome allocation problems to predict the optimal nutrient uptake and metabolic strategy as a function of carbon availability. We derive a Michaelis–Menten approximation of ABC transport, analytically demonstrating how the half-saturation concentration is a function of binding protein abundance. We predict that oligotrophs can attain nanomolar half-saturation concentrations using binding proteins with only micromolar dissociation constants and while closely matching transport and metabolic capacities. However, our model predicts that this requires large periplasms and that the slow diffusion of the binding proteins limits uptake. Thus, binding proteins are critical for oligotrophic survival yet severely constrain growth rates. We propose that this trade-off fundamentally shaped the divergent evolution of oligotrophs and copiotrophs.

Marine bacteria utilize carbon as a building block and an energy source and thus exert an important control on the amount of carbon that is sequestered in the ocean versus respired into the atmosphere. They use a spectrum of strategies to consume carbon: while copiotrophic bacteria dominate in nutrient-rich environments, oligotrophic bacteria dominate in nutrient-poor environments and are typically smaller, nonmotile, and slower growing. Yet the paragon oligotroph SAR11 is the planet’s most abundant organism. Despite this, most of our understanding of bacteria derives from research on copiotrophs. Here we use molecular-level models to understand how an oligotroph’s physiology enables it to outperform copiotrophs in nutrient-poor but not in nutrient-rich environments. We contrast copiotrophs’ prevalent method of sugar transport with oligotrophs’ reliance on binding proteins, which trap nutrients in the periplasm. Binding proteins allow cells to attain affinities that are much higher than the transport proteins’ intrinsic affinities. However, our model predicts that attaining such high affinities requires large periplasms with high abundances of the slowly diffusing binding proteins, which precludes high growth rates. By quantifying the benefits and costs of binding proteins, we provide a mechanistic explanation for the divergent evolution of oligotrophs and copiotrophs.

Resource uptake and the evolution of moderately efficient enzymes

Enzymes speed up reactions that would otherwise be too slow to sustain the metabolism of selfreplicators. Yet, most enzymes seem only moderately efficient, exhibiting kinetic parameters orders of magnitude lower than their expected physically achievable maxima and spanning over surprisingly large ranges of values. Here, we question how these parameters evolve using a mechanistic model where enzyme efficiency is a key component of individual competition for resources. We show that kinetic parameters are under strong directional selection only up to a point, above which enzymes appear to evolve under near-neutrality, thereby confirming the qualitative observation of other modeling approaches. While the existence of a large fitness plateau could potentially explain the extensive variation in enzyme features reported, we show using a population genetics model that such a widespread distribution is an unlikely outcome of evolution on a common landscape, as mutation–selection–drift balance occupy a narrow area even when very moderate biases towards lower efficiency are considered. Instead, differences in the evolutionary context encountered by each enzyme should be involved, such that each evolves on an individual, unique landscape. Our results point to drift and effective population size playing an important role, along with the kinetics of nutrient transporters, the tolerance to high concentrations of intermediate metabolites, and the reversibility of reactions. Enzyme concentration also shapes selection on kinetic parameters, but we show that the joint evolution of concentration and efficiency does not yield extensive variance in evolutionary outcomes when documented costs to protein expression are applied.

Novel insights into the organic solute transporter alpha/beta, OSTα/β: From the bench to the bedside

Organic solute transporter alpha/beta (OSTα/β) is a heteromeric solute carrier protein that transports bile acids, steroid metabolites and drugs into and out of cells. OSTα/β protein is expressed in various tissues, but its expression is highest in the gastrointestinal tract where it facilitates the recirculation of bile acids from the gut to the liver. Previous studies established that OSTα/β is upregulated in liver tissue of patients with extrahepatic cholestasis, obstructive cholestasis, and primary biliary cholangitis (PBC), conditions that are characterized by elevated bile acid concentrations in the liver and/or systemic circulation. The discovery that OSTα/β is highly upregulated in the liver of patients with nonalcoholic steatohepatitis (NASH) further highlights the clinical relevance of this transporter because the incidence of NASH is increasing at an alarming rate with the obesity epidemic. Since OSTα/β is closely linked to the homeostasis of bile acids, and tightly regulated by the nuclear receptor farnesoid X receptor, OSTα/β is a potential drug target for treatment of cholestatic liver disease, and other bile acid-related metabolic disorders such as obesity and diabetes. Obeticholic acid, a semi-synthetic bile acid used to treat PBC, under review for the treatment of NASH, and in development for the treatment of other metabolic disorders, induces OSTα/β. Some drugs associated with hepatotoxicity inhibit OSTα/β, suggesting a possible role for OSTα/β in drug-induced liver injury (DILI). Furthermore, clinical cases of homozygous genetic defects in both OSTα/β subunits resulting in diarrhea and features of cholestasis have been reported. This review article has been compiled to comprehensively summarize the recent data emerging on OSTα/β, recapitulating the available literature on the structure-function and expression-function relationships of OSTα/β, the regulation of this important transporter, the interaction of drugs and other compounds with OSTα/β, and the comparison of OSTα/β with other solute carrier transporters as well as adenosine triphosphate-binding cassette transporters. Findings from basic to more clinically focused research efforts are described and discussed.

Fibrinolytic protease from Bacillus cereus S46: Purification, characterization, and evaluation of its in vitro thrombolytic potential

Intravascular thrombosis is a prime cause of cardiac complications worldwide. Microbial fibrinolytic proteases are of clinical significance in thrombosis treatment. The present study discusses the purification and characterization of a protease from Bacillus cereus S46, ascertaining its in vitro thrombolytic activity against a blood clot. By the three-step purification involving precipitation, dialysis, and diethylaminoethyl-cellulose ion-exchange chromatography, a 12.37-fold purification of the enzyme to homogeneity was achieved. The apparent molecular mass of the protease was 30 kDa, as found by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The optimum activity of the enzyme was observed at pH 8.0 and 40°C. The enzyme retained an 82.19% residual activity at pH 8.0 and 40°C for 1 h. The K_m and V_max values of the protease with casein were 0.0027 mM and 9.712 µmol/min, respectively. In an in vitro assay, the purified protease resulted in 97.02% lysis of the blood clot. The fibrinolytic potential of the enzyme, together with its characteristics of being active and stable under near-physiological conditions, may suggest its application as a therapeutic agent.

Fermentation Products of Commensal Bacteria Alter Enterocyte Lipid Metabolism

Despite the recognized capacity of the gut microbiota to regulate intestinal lipid metabolism, the role of specific commensal species remains undefined. Here, we aimed to understand the bacterial effectors and molecular mechanisms by which Lactobacillus paracasei and Escherichia coli regulate lipid metabolism in enterocytes. We show that L-lactate produced by L. paracasei inhibits chylomicron secretion from enterocytes and promotes lipid storage by a mechanism involving L-lactate absorption by enterocytes, its conversion to malonyl-CoA, and the subsequent inhibition of lipid beta-oxidation. In contrast, acetate produced by E. coli also inhibits chylomicron secretion by enterocytes but promotes lipid oxidation by a mechanism involving acetate absorption by enterocytes, its metabolism to acetyl-CoA and AMP, and the subsequent upregulation of the AMPK/PGC-1α/PPARα pathway. Our study opens perspectives for developing specific bacteria- and metabolite-based therapeutic interventions against obesity, atherosclerosis, and malnutrition by targeting lipid metabolism in enterocytes.

Minimal exposure of lipid II cycle intermediates triggers cell wall antibiotic resistance

Cell wall antibiotics are crucial for combatting the emerging wave of resistant bacteria. Yet, our understanding of antibiotic action is limited, as many strains devoid of all resistance determinants display far higher antibiotic tolerance in vivo than suggested by the antibiotic-target binding affinity in vitro. To resolve this conflict, here we develop a comprehensive theory for the bacterial cell wall biosynthetic pathway and study its perturbation by antibiotics. We find that the closed-loop architecture of the lipid II cycle of wall biosynthesis features a highly asymmetric distribution of pathway intermediates, and show that antibiotic tolerance scales inversely with the abundance of the targeted pathway intermediate. We formalize this principle of minimal target exposure as intrinsic resistance mechanism and predict how cooperative drug-target interactions can mitigate resistance. The theory accurately predicts the in vivo efficacy for various cell wall antibiotics in different Gram-positive bacteria and contributes to a systems-level understanding of antibiotic action.