Biology subjects

Lercher, M.

Publications and source records attributed to Lercher, M..

14 recordsLinked to original sources

bioRxiv2026

Cell identities along the proximal-distal and micropylar-chalazal axes in the Arabidopsis heart-stage seed

* Seeds are complex reproductive organs consisting of diverse maternal and filial tissues. During development, the embryo and specialized tissues for nutrient storage required for seed germination and early seedling establishment emerge. * To explore the cellular diversity and differentiation of seeds, we performed single cell RNA-sequencing on heart stage Arabidopsis seeds and identified 20,097 cells that were grouped into [≥]21 distinct cell clusters. 20 of the 21 clusters were spatially assigned by combining bioinformatic analysis, imaging reporter fusion marker lines, and spatial transcriptomics. * Our analysis revealed a high degree of differentiation of epidermal cell and inner cell layers along the rotational and axial seed axes, highlighting the importance of cell position and ontogenesis. We identified unexpected spatial domains, including a cluster marked by abscission zone-specific transcripts, and a nucellar cluster shaped by developmentally programmed cell death. Surprisingly, embryo and endosperm showed similarities in transcript profiles despite distinct and complementary functions. * In summary, our findings establish seeds as a transcriptionally complex organ with high cell type heterogeneity and provide a basis for investigating the differentiation of diverse cell layers and spatial transcript profiles.

cell biology↗

bioRxiv2026

Growth costs of suboptimal protein allocation in nonlinear models of growing cells

Microbial growth depends on how cells allocate their limited proteome among competing functions. Although many proteins are expressed near levels that maximize growth, deviations from these optima are common and can impose substantial fitness costs. Modeling the growth costs of suboptimal protein allocation remains challenging because protein expression influences growth through multiple interacting mechanisms, including biosynthetic demands, enzyme kinetics, and limits on cellular density. Here we analyze these effects using growth balance analysis (GBA), a nonlinear framework that predicts steady-state growth and biomass composition of coarse-grained microbial models from basic cellular constraints. The optimal biomass compositions predicted for these models include not only protein concentrations but also the reactant concentrations required to saturate enzymes through nonlinear rate laws, thereby capturing more complex resource trade-offs than in conventional linear resource allocation models. Using these GBA models, we compute optimal biomass compositions with individual proteins fixed at suboptimal levels. The resulting relationships between suboptimal protein allocation and growth rate are consistent with qualitative experimental patterns in bacteria, with growth effects depending on protein function (including idle proteins), environmental conditions, toxic byproducts, and alternative reactions. These results indicate a practical and tractable approach for modeling growth costs arising from suboptimal protein allocation, and suggest a basis for predictive modeling in metabolic engineering and synthetic biology.

systems biology↗

bioRxiv2026

Predicting turnover number fold-changes to recover true mutation effects and overcome biases in mutant datasets

Machine learning is increasingly used to guide protein engineering by predicting how mutations affect desired properties. Recent models for the turnover number (kcat) of enzymes report high accuracy, suggesting that mutation effects can be inferred directly from protein sequence. However, these approaches are typically evaluated on heterogeneous datasets of enzyme variants, where closely related sequences and systematic reporting patterns may confound model performance. A central challenge is therefore to determine whether current models truly capture mutation-specific effects or instead exploit statistical regularities in the data. Here we show that much of the reported accuracy in mutant kcat prediction arises from two pervasive biases: variants of the same enzyme occupy a narrow activity range, and mutations within a group often share a common direction of change. Simple baselines that exploit these biases match or exceed the performance of existing models, indicating that high apparent accuracy does not imply mechanistic understanding. To address this limitation, we introduce a bias-aware framework that reformulates prediction as a pairwise fold-change task and evaluates performance on unseen mutant-mutant pairs, thereby isolating mutation-specific signal. A proof-of-principle implementation explains approximately one-third of the variance under these conditions and outperforms existing models on leakage-controlled benchmarks. More broadly, this work establishes a general strategy for evaluating and modeling mutation effects in biochemical datasets, with implications for protein engineering and related fields.

bioinformatics↗

bioRxiv2026

FoldVision: A compute-efficient atom-level 3D protein encoder

Protein function emerges from three-dimensional structure, yet many large-scale protein prediction pipelines still rely solely on linear sequence embeddings. Although multiple structure-aware protein networks have been proposed, they often omit atom-level details and struggle to capture the detailed chemistry of binding sites. Here, we introduce FoldVision, a compute-efficient 3D convolutional neural network that voxelizes every heavy atom, learns rotation-robust representations, and is pre-trained on over 500 000 AlphaFold-2 structures, which is more than two orders of magnitude less data than used to train modern protein language models. Despite its compact size of 123 million parameters, FoldVision outperforms or matches state-of-the-art protein encoders on four benchmarks that require fine structural resolution: enzyme-substrate classification, transporter-substrate classification, drug-kinase inhibition, and drug-target inhibition prediction. A simple ensemble with a sequence-based model consistently improves performance across all benchmarks beyond any individual model. This indicates that FoldVision learns structural signals that are complementary to those extracted by sequence-based models. This study demonstrates that full-atom protein 3D CNNs are both tractable and superior to protein language models alone for structure-dependent tasks.

biochemistry↗

bioRxiv2023

Growth balance analysis models of cyanobacteria for understanding resource allocation strategies

Cyanobacteria have emerged as attractive microbial cell factories, as they can convert atmospheric CO2 and sunlight into valuable chemicals. To increase their growth and productivity, one should aim to optimize the allocation of limited cellular resources across different metabolic processes. Here, we developed two growth balance analysis (GBA) models for the cyanobacterium Synechocystis sp. PCC 6803. In its biological assumptions, the models closely related to an existing coarse-grained model, while its mathematical formulation is heavily streamlined. We show that the GBA models provide virtually identical predictions about cellular resource allocation among photosynthesis, carbon metabolism, and the protein translation machinery under different environmental conditions as the previous, mathematically more involved model. Our model also captures the effects of photodamage on proteome allocation and the resulting growth rates. We further show how the GBA model can be easily extended to include more reactions, leading to a second GBA model capable of new predictions about the cellular resource allocation. Balanced growth models of the type presented here can easily expanded to include more biological details, providing a useful toolbox for the understanding of the physiological capabilities of cyanobacteria, their allocation of cellular resources, and the potential of their bioengineering for optimized biomass production.

systems biology↗

bioRxiv2022

Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow

Understanding the allocation of the cellular proteome to different cellular processes is central to unraveling the organizing principles of bacterial physiology. Proteome allocation to protein translation itself is maximally efficient, i.e., it represents the minimal allocation of dry mass able to sustain the observed protein production rate. In contrast, recent studies on bacteria have demonstrated that the concentrations of many proteins exceed the minimal level required to support the observed growth rate, indicating some heterogeneity across pathways in their proteome efficiency. Here, we systematically analyze the proteome efficiency of metabolic pathways, which together account for more than half of the E. coli proteome during exponential growth. Comparing the predicted minimal and the observed proteome allocation to different metabolic pathways across growth conditions, we find that the most costly biosynthesis pathways - those for amino acid biosynthesis and cofactor biosynthesis - are expressed for near optimal efficiency. Overall, proteome efficiency increases along the carbon flow through the metabolic network: proteins involved in pathways of nutrient uptake and central metabolism tend to be highly over-abundant, while proteins involved in anabolic pathways and in protein translation are much closer to the expected minimal abundance across conditions. Our work thus provides a birds-eye view of metabolic pathway efficiency, demonstrating systematic deviations from optimal cellular efficiency at the network level. ImportanceProtein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in E. coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, are near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.

systems biology↗

bioRxiv2020

Optimal density of biological cells

A substantial fraction of the bacterial cytosol is occupied by catalysts and their substrates. While a higher volume density of catalysts and substrates might boost biochemical fluxes, the resulting molecular crowding can slow down diffusion, perturb the reactions Gibbs free energies, and reduce the catalytic efficiency of proteins. Due to these tradeoffs, dry mass density likely possesses an optimum that facilitates maximal cellular growth and that is interdependent on the cytosolic molecule size distribution. Here, we analyze the balanced growth of a model cell, accounting systematically for crowding effects on reaction kinetics. Its optimal cytosolic volume occupancy depends on the nutrient-dependent resource allocation into large ribosomal vs. small metabolic macromolecules, reflecting a tradeoff between the saturation of metabolic enzymes, favoring larger occupancies with higher encounter rates, and the inhibition of the ribosomes, favoring lower occupancies with unhindered diffusion of tRNAs. Our predictions across growth rates are quantitatively consistent with the experimentally observed reduction in volume occupancy on rich media compared to minimal media in E. coli. Strong deviations from optimal cytosolic occupancy only lead to minute reductions in growth rate, which are nevertheless evolutionarily relevant due to large bacterial population sizes. In sum, cytosolic density variation in bacterial cells appears to be consistent with an optimality principle of cellular efficiency.

biophysics↗

bioRxiv2020

Structure of autosynthetic models of balanced cell growth and numerical optimization of their growth rate

Genome-scale reaction network models are available for many prokaryotic organisms. Yet, to predict the proteome and metabolome of the cell from them, additional information about (i) the nonlinear enzyme kinetics and (ii) the regulation of protein expression by metabolic signals is necessary. Knowledge about the latter could be sidestepped by assuming that expression regulation has evolved to achieve the protein composition that maximizes cellular growth rate. A general mathematical framework for optimizing the growth rate of models comprising an arbitrarily complex metabolic network and a relatively simple protein-synthesis network was recently formulated independently by two research groups [de Groot et al., PLoS Comput. Biol. 16, e1007559 (2020); Dourado & Lercher, Nature Commun. 11, 1226 (2020)]. Here, this formalism is further developed with particular focus on carrying out the optimization numerically. To this end, we identify the concentrations of the enzymes as the independent variables of the optimization problem and propose novel multiplicative updates for the iterative calculation of the dependent metabolite concentrations. The reduced gradient method, with analytical derivatives, is employed for the numerical optimization. Additionally, the roles of the dilution of the metabolite concentrations by growth and the commonly invoked constraint on the cell dry mass density are clarified. These developments should lay the basis for the practical optimization of large-scale kinetic models, thus formally connecting the physiological "macrostate" of the cell, characterized by its growth rate, to its "microstate", described by the cell proteome and metabolome. Author summaryAn evolving population of non-interacting, unicellular organisms in a constant environment will maximize its growth rate. By expressing the growth rate as a mathematical function of the cellular composition, it becomes possible to formulate an optimization problem whose solution yields the cell proteome and metabolome at the maximal growth rate. The formulation and solution of such an optimization problem has the potential to elucidate fundamental optimality principles in living cells and to enable the engineering of complex biological systems. Building on previous work, here we address the task of solving this optimization problem numerically. In the process, we elucidate the mathematical role of some common simplifying approximations. This allows us to organize many of the existing formulations of the optimization problem into a hierarchy, whose lower levels are reached by invoking these approximations.

systems biology↗

bioRxiv2020

Unique and distinct identities and functions of leaf phloem cells revealed by single cell transcriptomics

AO_SCPLOWBSTRACTC_SCPLOWThe leaf vasculature plays a key role in solute translocation. Veins consist of at least seven distinct cell types, with specific roles in transport, metabolism, and signaling. Little is known about the vascular cells in leaves, in particular the phloem parenchyma (PP). PP effluxes sucrose into the apoplasm as a basis for phloem loading; yet PP has only been characterized microscopically. Here, we enriched vascular cells from Arabidopsis leaves to generate a single-cell transcriptome atlas of leaf vasculature. We identified [≥]19 cell clusters, encompassing epidermis, guard cells, hydathodes, mesophyll, and all vascular cell types, and used metabolic pathway analysis to define their roles. Clusters comprising PP cells were enriched for transporters, including SWEET11 and SWEET12 sucrose and UmamiT amino acid efflux carriers. PP development occurs independently from APL, a transcription factor required for phloem differentiation. PP cells have a unique pattern of amino acid metabolism activity distinct from companion cells (CC), explaining differential distribution/metabolism of amino acids in veins. The kinship relation of the vascular clusters is strikingly similar to the vein morphology, except for a clear separation of CC from the other vascular cells including PP. In summary, our scRNA-seq analysis provides a wide range of information into the leaf vasculature and the role and relationship of the leaf cell types.

plant biology↗

bioRxiv2020

Selective attention in hypothesis-driven data analysis

When analyzing the results of an experiment, the mental focus on a specific hypothesis might prevent the exploration of other aspects of the data, effectively blinding one to new ideas. To test this notion, we performed an experiment in which we asked undergraduate students to analyze a fictitious dataset. In addition to being asked what they could conclude from the dataset, half of the students were asked to also test specific hypotheses. In line with our notion, students in the hypothesis-free group were almost 5 times more likely to observe an image of a gorilla when simply plotting the data, a proxy for an initial step towards data analysis. If these findings are representative also of scientific research as a whole, they warrant concern about the current emphasis on hypothesis-driven research, especially in the context of information-rich datasets such as those now routinely created in the biological sciences. Our work provides evidence for a link between the psychological effect of selective attention and hypothesis-driven data analysis, and suggests a hidden cost to having a hypothesis when analyzing a dataset.

scientific communication and education↗

bioRxiv2018

Modeling Cellular Resource Allocation Reveals Low Phenotypic Plasticity of C4 Plants and Infers Environments of C4 Photosynthesis Evolution

- The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extend do they reflect ancestral environments? Here, we explore these questions for C3, C4, and C3-C4 intermediate plants of the model genus Flaveria.\n- We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns.\n- To achieve maximal CO2 fixation rates under growth conditions differing from those experienced during their evolution, C4 species need to re-allocate significantly more nitrogen between photosynthetic components than their C3 relatives. As this is linked to a limited phenotypic plasticity, observed resource distributions in C4 plants still reflect optimality in ancestral environments, allowing their quantitative inference.\n- Our work allows us to quantify environmental effects on resource allocation and performance of photosynthetic organisms. This understanding paves the way for interpreting present photosynthetic physiology in the light of evolutionary history.

plant biology↗

bioRxiv2018

Different alleles of the same gene vary in pleiotropy, often mediated through currency metabolite production

A major obstacle to the mapping of genotype-phenotype relationships is pleiotropy, the tendency of mutations to affect seemingly unrelated traits. Pleiotropy has major implications for evolution, development, ageing, and disease. Except for disease data, pleiotropy is almost exclusively estimated from full gene knockouts. However, most deleterious alleles segregating in natural populations do not fully abolish gene function, and the degree to which a polymorphism reduces protein function may influence the number of traits it affects. Utilizing genome-scale metabolic models for Escherichia coli and the bakers yeast Saccharomyces cerevisiae, we show that most fitness-reducing full gene knockouts of metabolic genes have pleiotropic effects, i.e., they compromise the production of multiple biomass components. Alleles of the same gene with increasingly reduced enzyme function typically affect an increasing number of biomass components. This increasing pleiotropy is often mediated through effects on the generation of currency metabolites such as ATP or NADPH. We conclude that the physiological effects observed in full gene knockouts will in most cases not be representative for alleles with only partially reduced enzyme capacity or expression level.

systems biology↗

bioRxiv2018

Automated high-quality reconstruction of metabolic networks from high-throughput data

While new genomes are sequenced at ever increasing rates, their phenotypic analysis remains a major bottleneck of biomedical research. The generation of genome-scale metabolic models capable of accurate phenotypic predictions is a labor-intensive endeavor; accordingly, such models are available for only a small percentage of sequenced species. The standard metabolic reconstruction process starts from a (semi-)automatically generated draft model, which is then refined through extensive manual curation. Here, we present a novel strategy suitable for full automation, which exploits high-throughput gene knockout or nutritional growth data. We test this strategy by reconstructing accurate genome-scale metabolic models for three strains of Streptococcus, a major human pathogen. The resulting models contain a lower proportion of reactions unsupported by genomic evidence than the most widely used E. coli model, but reach the same accuracy in terms of knockout prediction. We confirm the models predictive power by analyzing experimental data for auxotrophy, additional nutritional environments, and double gene knockouts, and we generate a list of potential drug targets. Our results demonstrate the feasibility of reconstructing high-quality genome-scale metabolic models from high-throughput data, a strategy that promises to massively accelerate the exploration of metabolic phenotypes.\n\nSignificance statementReading bacterial genomes has become a cheap, standard laboratory procedure. A genome by itself, however, is of little information value - we need a way to translate its abstract letter sequence into a model that describes the capabilities of its carrier. Until now, this endeavor required months of manual work by experts. Here, we show how this process can be automated by utilizing high-throughput experimental data. We use our novel strategy to generate highly accurate metabolic models for three strains of Streptococcus, a major threat to human health.

systems biology↗

bioRxiv2017

The adaptive acquisition of single DNA segments drives metabolic evolution across E. coli lineages

Even closely related prokaryotes show an astounding diversity in their ability to grow in different nutritional environments1,2. Mechanistically, this diversity arises predominantly through horizontal gene transfer, the exchange of DNA between individuals from different strains3. It has been hypothesized that complex metabolic adaptations - those requiring the acquisition of multiple distinct DNA segments - can evolve via selectively neutral intermediate steps4,5; an alternative explanation rests on the existence of intermediate environments that make each individual DNA acquisition adaptive6 However, it is unclear how important changing environments6 are compared to neutral explorations of phenotype space5; more fundamentally, it is unknown what fraction of metabolic adaptations are indeed complex. Here, we use metabolic network simulations to show that all 3,363 phenotypic innovations observed in the evolutionary history of 53 E. coli strains arose through the acquisition of a single DNA segment; while we found no evidence for the contribution of selectively neutral processes, 10.6% of adaptations to previously unviable environments relied on the support of DNA acquisitions on earlier phylogenetic branches. 97.0% of all metabolic phenotypes accessible for the E. coli pan-genome could be bestowed on any ancestral strain by transferring a single DNA segment from one of the extant strains. These results demonstrate an amazing ability of the E. coli lineage to quickly adapt to previously inaccessible environments through a single DNA acquisition, an ability likely to be mirrored in other clades of generalist bacteria.

systems biology↗