Biology subjects

Luthey-Schulten, Z.

Publications and source records attributed to Luthey-Schulten, Z..

12 recordsLinked to original sources

bioRxiv2026

Full-length transcriptomics and proteomics reveal how genome minimization reshapes gene expression in synthetic bacteria

JCVI-syn1.0 (Syn1.0) and JCVI-syn3A (Syn3A), genetically synthetic and genome-reduced versions of the naturally occurring bacterium Mycoplasma mycoides, are landmark platforms for defining the gene set required for life, yet how their genomes are expressed at the RNA level remains uncharacterized. We combined full-length PacBio and native long-read RNA sequencing with short-read quantification and complementary proteomics to map transcription, RNA processing, and protein abundance in both cells. In Syn1.0, full-length sequencing resolved 459 operons encompassing 911 genes, revealing pervasive RNA processing with a strong 3' bias. Analysis of the division and cell-wall cluster showed how transcriptional context explained the restoration of genes required for normal cell division in Syn3A. Most antisense and intergenic transcription in Syn1.0 reflected low-level transcriptional noise arising from inherited mis-annotation, read-through, and synthetic sequences. Much of that transcription was lost in Syn3A after genome minimization. Reducing the genome unexpectedly altered the expression of several retained genes by deleting promoters, most prominently reducing expression of the nucleoid protein HupA and central-carbon enzymes. Meanwhile, one third of the coding mRNA pool was allocated to a single 21-gene ribosomal-protein operon, while several other ribosomal proteins had reduced transcript abundance, which possibly led to imbalanced ribosome assembly. Expression of RNA polymerase and central-carbon metabolism declined at both mRNA and protein levels whereas RNase Y degradosome abundance increased. These shifts in the synthetic evolution of Syn3A suggest plausible mechanisms for its reduced chromosome contacts and slower growth. Together, these results show that genome minimization alters not only gene content but also the transcriptional context and resource allocation of retained genes. The analysis and visualization that are shared via Jupyter Notebook provide the RNA-level foundation for whole-cell modeling of the minimal cell.

synthetic biology↗

bioRxiv2026

Spatiotemporal 4D Whole-cell Modeling of a Minimal Autotroph Reveals Central Carbon Metabolism Regulated Locally by Protein Megacomplexes via Post-translational Modifications under Light Disturbance

Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET images as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the "dark complex," modulate metabolic flux at a conserved regulatory node of the Calvin-Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4. Significance StatementThis work advances 4D whole-cell modeling by presenting the first spatiotemporal simulation of a photosynthetic autotroph using the Lattice Microbes platform. Using Prochlorococcus marinus MED4, we show that subcellular spatial organization of organelles, diffusion constraints, and redox regulation collectively shape central carbon metabolism across orders of magnitude in space and time. Through a perturbation-based strategy that generates multi-omics data sets over time, we construct and validate a spatially and temporally resolved model of MED4, constrained by high-resolution (10 nm) cryo-electron tomography. Our results highlight the importance of localized biochemical reactions and redox-dependent post-translational modification of enzymes in regulating carbon fixation in a noisy environment under light disturbance. This study establishes a spatiotemporal, whole-cell physiology modeling framework as a transformative tool for uncovering multiscale regulatory responses to environmental gradients.

systems biology↗

bioRxiv2025

COTree: A Statistical Framework for Deciphering Cell-Resolved Multi-Omics Trajectories

Recent advances in whole-cell modeling enable the computational tracking of the temporal evolution of thousands of molecular species across genomic, transcriptomic, proteomic, and metabolomic layers. These models provide a complementary perspective for studying cellular dynamics, offering continuous, system-wide observations that are difficult to obtain from experimental technologies, which are often destructive and yield only static measurements from limited modalities. While whole-cell models generate multi-omic simulation trajectories with high temporal resolution, analyzing and interpreting such complex data remains a major challenge that limits their potential to elucidate cellular dynamics. To address this challenge, we propose COTree, a statistical framework that learns integrated multi-omic representations and constructs a trajectory principal tree to summarize cellular progression patterns. COTree enables a broad range of downstream analyses, including cell classification, fate prediction, developmental time detection, and driver species identification, that provide new insights into how cells develop and differentiate. To demonstrate its practical utility, we apply COTree to a multi-omic trajectory dataset generated from the whole-cell model of JCVI-Syn3A, revealing cell types, characterizing long-term cellular dynamics, and identifying key driver species associated with cell death and replication.

bioinformatics↗

bioRxiv2025

Unraveling the Transcriptional Landscape within a Minimized Bacterium via Comparative Analysis

Stochastic nature of gene expression leads to the complex formation of the bacterial transcriptome and proteome. In contrast to typical transcriptome studies, we employ a near wild-type, Syn1.0, of the naturally genome-reduced Mycoplasmas, and the dramatically further genome-reduced JCVI-syn3A thus avoiding additional contributions from many non-essential cellular functions. To aid in profiling the transcriptional landscape within these bacteria, we present a bioinformatic analysis of the genetic sequence motifs implicated in modulating the stochastic gene expression events, coupled with genome-wide short-read (Illumina) and long-read (Oxford Nanopore Technologies and Pacfic Biosciences) RNA sequencing. The bioinformatic analysis coupled with information from structural studies assigns strengths of the Shine-Dalgarno signatures and identifies both transcription initiation and termination sites, leading to predictions of RNA isoforms in Syn1.0 (and related organisms). The long-read and short-read RNA sequencing characterized the predicted transcriptional activity, and the long-read methods provide direct insight into the RNA isoform complexity within Syn1.0. Comparison of the RNA sequencing results with that of the bioinformatic analysis highlights the inability of bioinformatics alone to capture the results of bacterial transcription without including effects of RNA degradation. This study emphasizes the need for comparative analysis and potential dangers of genome reduction, exemplified through the discovery of altered gene expression patterns of JCVI-syn1.0 and JCVI-syn3A, achieved via the union of our transcriptome study with their proteomics data. Analysis of the transcriptomics data sets through a Jupyter notebook allows any genomic region to be easily examined. Table of Content Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/681674v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@78a9e0org.highwire.dtl.DTLVardef@1d8c9fcorg.highwire.dtl.DTLVardef@1b4e129org.highwire.dtl.DTLVardef@2a8c3f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Assembly of Macromolecular Complexes in the Whole-Cell Model of a Minimal Cell

Macromolecular complexes in the genetically minimized bacterium, JCVI-syn3A, support gene expression (RNA polymerase, ribosome, degradosome), metabolism (ABC transporters, ATP synthase) and chromosome dynamics. In this work, we further incorporate the assembly of 21 unique macromolecular complexes into the existing whole-cell kinetic model of Syn3A. The synthesis and translocation of protein subunits in membrane complexes occur through distinct pathways. A range of 2D association rates for membrane complexes were considered to guarantee a high yield of assembly given the existing time scales of gene expression. By alleviating the undesired kinetically trapped intermediates in ATP synthase assembly, the efficiency was improved. The assembly of RNA polymerase, ribosome, and degradosome influence the speed and efficiency of protein synthesis. Collectively, this model predicted time-dependent cellular behaviors consistent with experiments. A machine learning analysis of the time-dependent metabolomics and metabolic fluxes highlighted the effect of introducing complex assembly into our whole-cell model.

biophysics↗

bioRxiv2025

Bringing the Genetically Minimal Cell to Life on a Computer in 4D

We present a whole-cell spatial and kinetic model for the 100 minute cell cycle of the genetically minimal bacterium, JCVI-syn3A. This is the first simulation of a complete cell cycle in 4D including all genetic information processes, metabolic networks, growth, and cell division. Integrating hybrid computational methods, dynamics of the morphological transformations were achieved. Growth is driven by synthesis of lipids and membrane proteins and constrained by new fluorescence imaging data. Chromosome replication and segregation is controlled by essential SMC and topoisomerase proteins in Brownian dynamics simulations with replication rates responding to dNTP pools from metabolism. The model captures the origin to terminus ratio measured in our DNA sequencing and recovers other experimental measurements like doubling time, mRNA half-lives, protein distributions, and ribosome counts. Because of stochasticity, each replicate cell is unique. Not only do we predict average behavior for partitioning to daughter cells, we predict the heterogeneity among them.

biophysics↗

bioRxiv2021

Metabolite Damage and Damage-Control in a Minimal Genome

Analysis of the genes retained in the minimized Mycoplasma JCVI-Syn3A genome established that systems that repair or preempt metabolite damage are essential to life. Several genes with known metabolite damage repair or preemption functions were identified and experimentally validated, including 5-formyltetrahydrofolate cyclo-ligase, CoA disulfide reductase, and certain hydrolases. Furthermore, we discovered that an enigmatic YqeK hydrolase domain fused to NadD has a novel proofreading function in NAD synthesis and could double as a MutT-like sanitizing enzyme for the nucleotide pool. Finally, we combined metabolomics and cheminformatics approaches to extend the core metabolic map of JCVI-Syn3A to include promiscuous enzymatic reactions and spontaneous side reactions. This extension revealed that several key metabolite damage-control systems remain to be identified in JCVI-Syn3A, such as that for methylglyoxal.

microbiology↗

bioRxiv2020

Effects of individual base-pairs on in vivo target search and destruction kinetics of small RNA

Base-pairing interactions mediate intermolecular target recognition in many biological systems and applications, including DNA repair, CRISPR, microRNA, small RNA (sRNA) and antisense oligo therapies. Even a single base-pair mismatch can cause a substantial difference in biological activity but presently we do not yet know how the target search kinetics in vivo are influenced by single nucleotide level changes. Here, we used high-throughput sequencing to identify functionally relevant single point mutants of the bacterial sRNA, SgrS, and quantitative super-resolution microscopy to probe the mutational impact on the regulation of its primary target, ptsG mRNA. Our super-resolution imaging and analysis platform allowed us to further dissect mutational effects on SgrS lifetimes, and even subtle changes in the in vivo rates of target association, kon, and dissociation, koff. Mutations that disrupt binding of a chaperone protein, Hfq, and are distal to the mRNA annealing region still decreased kon and increased koff, providing an in vivo demonstration that Hfq directly facilitates sRNA-mRNA annealing. Single base-pair mismatches in the annealing region reduced kon by 24-31% and increased koff by 14-25%, extending the time it takes to find and destroy the target mRNA by about a third, depending on whether an AU or GC base-pair is disrupted. The effects of disrupting contiguous base-pairing are much more modest than that expected from thermodynamics, suggesting that Hfq also buffers base-pair disruptions.

molecular biology↗

bioRxiv2020

Stochastic Analysis Probes the Role of the Chaperone Hfq in E. coli Sugar Shock Response

Small RNAs (sRNAs) play a crucial role in the regulation of bacterial gene expression by silencing the translation of target mRNAs. SgrS is an sRNA that relieves glucose-phosphate stress, or "sugar shock" in E. coli. The power of single cell measurements is their ability to obtain population level statistics that illustrate cell-to-cell variation. Here, we utilize single molecule super-resolution microscopy in single E. coli cells coupled with stochastic modeling to analyze glucose-phosphate stress regulation by SgrS. We present a kinetic model that captures the combined effects of transcriptional regulation, gene replication and chaperone mediated RNA silencing in the SgrS regulatory network. This more complete kinetic description, simulated stochastically, recapitulates experimentally observed cellular heterogeneity and characterizes the binding of SgrS to the chaperone protein Hfq as a slow process that not only stabilizes SgrS but also may be critical in restructuring the sRNA to facilitate association with its target ptsG mRNA.

biophysics↗

bioRxiv2020

Bacteria on steroids: the enzymatic mechanism of an NADH-dependent dehydrogenase that regulates the conversion of cortisol to androgen in the gut microbiome

Microorganisms extensively modify host steroids, but whether these reactions merely eliminate hormones or create signals with new biological identities is largely unknown. Gut bacteria have been known for more than four decades to reduce cortisol to 20-dihydrocortisol, yet the physiological consequence of this transformation remained unresolved. Here we show that microbial cortisol reduction creates a distinct host signalling molecule. A 2.0-[A] structure of the bacterial enzyme DesC, together with molecular dynamics, biochemical perturbation and hybrid quantum mechanics/molecular mechanics simulations, defines substrate recognition and an ordered hydride-transfer and proton-relay mechanism. In gnotobiotic mice, isogenic bacteria expressing active DesC--but not a catalytically inactive S47A variant produced 20-dihydrocortisol in the intestine and circulation and reprogrammed colonic transcription. In primary intestinal epithelial cells, 20-dihydrocortisol, but not cortisol, activated ERK-dependent inflammatory and growth-associated programmes. These responses required nuclear receptor subfamily 4 group A member 3 (NR4A3), whose purified ligand-binding domain bound 20-dihydrocortisol but showed no detectable binding to cortisol. A Chicago colonoscopy cohort linked chronic cortisol exposure, faecal desC, the microbial metabolite and colorectal phenotypes. Thus, bacterial metabolism can change receptor selectivity and biological activity rather than simply terminate host hormone action, expanding the endocrine chemistry of the host.

microbiology↗

bioRxiv2019

Complete structure of the core signalling unit of the E. coli chemosensory array in an optimised minicell strain

Motile bacteria sense chemical gradients with transmembrane receptors organised in supramolecular signalling arrays.1,2 Understanding stimulus detection and transmission at the molecular level requires precise structural characterisation of the array building block known as a core signalling unit (CSU). Here we introduce a novel E. coli strain that forms small minicells possessing extended and highly ordered chemosensory arrays. We provide a three-dimensional (3D) map of a complete CSU at ~16 [A] resolution by cryo-electron tomography (cryo-ET) and subtomogram averaging. This map, combined with previously determined high resolution structures and molecular dynamics simulations, yields an atomistic model of the membrane-bound CSU and enables spatial localisation of its signalling domains. Our work thus offers a solid structural basis for interpretation of existing data and design of new experiments to elucidate signalling mechanisms within the CSU and larger array.

cell biology↗

bioRxiv2018

An In-Silico Mammalian Whole-Cell Model Reveals the Influence of Spatial Organization on RNA Splicing Efficiency

Spatial organization is a characteristic of eukaryotic cells, achieved by utilizing both membrane-bound and non-bound organelles. We model the effects of this organization and of organelle heterogeneity on RNA splicing (the process of making translationally-ready messenger RNA) and on splicing particles (the building blocks of splicing machinery) in mammalian cells. We constructed a spatially-resolved whole HeLa cell model from various experimental data and developed reaction networks to describe the RNA splicing processes. We incorporated these networks into our whole-cell model and performed stochastic simulations for up to 15 minutes of biological time. We find that the number of nuclear pore complexes affects the number of assembled splicing particles; that a slight increase of splicing particle localization in nuclear speckles (non-membrane-bound or- ganelles) leads to disproportionate enhancement in the mRNA splicing and reduction in the transcript noise; and that compartmentalization is critical for a correctly-assembled particle yield. Our model also predicts that the distance between genes and speckles has a considerable effect on effective mRNA production rate, further emphasizing the importance of genome organization around speckles. The HeLa cell model, including organelles and subcompartments, provides an adaptable foundation to study other cellular processes which are strongly modulated by spatio-temporal heterogeneity.\n\nSignificance StatementThe spliceosome is one of the most complex cellular machineries that cuts and splices the RNA code in eukaryotic cells. It dynamically assembles, disassembles, and its components are formed in multiple compartments. The efficiency of splicing process depends on localization of its components in nuclear membrane-less organelles. Therefore, a computational model of spliceosomal function must contain a spatial model of the entire cell. However, building such a model is a challenging task, mainly due to the lack of homogeneous experimental data and a suitable computational framework. Here, we overcome these challenges and present a whole HeLa cell model, with nuclear, subnuclear, and extensive cytoplasmic structures. The three-dimensional model is supplemented by reaction-diffusion processes to shed light on the function of the spliceosome.

biophysics↗