Nucleoid compaction in bacteria is commonly attributed to cytoplasmic crowding, DNA supercoiling, and nucleoid-associated proteins (NAPs). In most bacterial species, including E. coli, these effects condense the chromosome into a subcellular region and largely exclude ribosomes to the surrounding cytoplasm. In contrast, many Mycoplasma-including the Mycoplasma-derived synthetic cell JCVI-Syn3A-exhibit a cell-spanning nucleoid with ribosomes distributed throughout. Because Mycoplasma are evolutionarily distant from model bacteria like E. coli and have undergone extensive genome reduction, Syn3A is a natural testbed for genotype-to-'physiotype'-to-phenotype, in which genome-encoded composition reshapes cell-scale organization. Here we show that this organization can arise from Syn3A's unusually high abundance of positively charged proteins. We develop a coarse-grained model that explicitly and physically represents a sequence-accurate chromosome together with ribosomes and cytoplasmic proteins at physiological size, charge, and abundance. With DNA and ribosomes alone, the cell-spanning nucleoid relaxes toward a compacted state that sterically excludes ribosomes, indicating missing physics beyond polymer mechanics and excluded volume. When we include electrostatic interactions by assigning effective charges to each biomolecule, positively charged proteins dynamically enrich around ribosomes and DNA, partially screening ribosome-DNA repulsion. This charge shielding enables ribosomes to penetrate the nucleoid mesh and stabilizes a cell-spanning nucleoid consistent with experiment. This behavior is robust across parameter sweeps: DNA stiffness, heterogeneous mesh size, and crowding favor compaction, whereas electrostatics and size polydispersity promote expansion, with consequences for migration pathways within the nucleoid and thus transcription-translation dynamics. The framework is parameterized directly from genomic and proteomic composition and is transferable to other bacteria.
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Gesse Roure
Vishal S. Sivasankar
Roseanna N. Zia
PLoS Computational Biology
University of Missouri
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Roure et al. (Tue,) studied this question.
www.synapsesocial.com/papers/69a75abfc6e9836116a20fb3 — DOI: https://doi.org/10.1371/journal.pcbi.1013898
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