Search arXivSearch

arXiv · 2104.09105

Steric interactions and out-of-equilibrium processes control the internal organization of bacteria

Abstract

Despite the absence of a membrane-enclosed nucleus, the bacterial DNA is typically condensed into a compact body - the nucleoid. This compaction influences the localization and dynamics of many cellular processes including transcription, translation, and cell division. Here, we develop a model that takes into account steric interactions among the components of the Escherichia coli transcriptional-translational machinery (TTM) and out-of-equilibrium effects of mRNA transcription, translation, and degradation, in order to explain many observed features of the nucleoid. We show that steric effects, due to the different molecular shapes of the TTM components, are sufficient to drive equilibrium phase separation of the DNA, explaining the formation and size of the nucleoid. In addition, we show that the observed positioning of the nucleoid at midcell is due to the out-of-equilibrium process of messenger RNA (mRNA) synthesis and degradation: mRNAs apply a pressure on both sides of the nucleoid, localizing it to midcell. We demonstrate that, as the cell grows, the production of these mRNAs is responsible for the nucleoid splitting into two lobes, and for their well-known positioning to 1/4 and 3/4 positions on the long cell axis. Finally, our model quantitatively accounts for the observed expansion of the nucleoid when the pool of cytoplasmic mRNAs is depleted. Overall, our study suggests that steric interactions and out-of-equilibrium effects of the TTM are key drivers of the internal spatial organization of bacterial cells.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ander Movilla Miangolarra, Sophia Hsin-Jung Li, Jean-François Joanny, Ned S. Wingreen, Michele Castellana. 2021-04-19. Steric interactions and out-of-equilibrium processes control the internal organization of bacteria. https://doi.org/10.1073/pnas.2106014118

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Local intercellular coupling is sufficient for long-range calcium signaling

Long-range intercellular calcium (Ca2+) signaling coordinates biological processes ranging from fertilization to contraction and cell death. The classical model attributes this long-range propagation to rapid diffusion of inositol 1,4,5-trisphosphate (IP3) through gap junctions. However, recent evidence that IP3 diffuses far more slowly than previously believed, and that Ca2+ oscillations persist even when gap junctions are disassembled, indicates that an alternative mechanism must sustain long-range communication. Here we develop a computational model showing that local coupling between neighboring cells is sufficient to generate and propagate regenerative Ca2+ oscillations across a cell population without fast molecular diffusion. Each cell is treated as an oscillator whose intrinsic frequency is set by its local IP3 concentration through an IP3-dependent refractory period, and neighboring cells are coupled using a Kuramoto nearest-neighbor framework. In a dual-stiffness regime, cells on a stiff extracellular matrix entrain their soft-matrix neighbors, producing an offset traveling wave of Ca2+ release. This reproduces the finite spatial range of influence (~8 cell lengths) observed experimentally. Our findings propose a diffusion-independent paradigm for calcium signaling in which local intercellular coupling drives long-range communication, offering insight into how localized ECM stiffening in asthma and fibrosis may produce systemic effects.

q-bio.CB

Fragmented uptake drives lipid accumulation in macrophage cannibalistic efferocytosis

Efferocytosis, the clearance of dying cells typically by macrophages, is essential for tissue homeostasis and the resolution of inflammation. Previous experiments by Ford et al. (Proc. R. Soc. B, 2019) showed that cannibalistic efferocytosis redistributes endogenous lipid from dying macrophages into the surviving population, but existing mathematical models do not reproduce the observed population dynamics and lipid distributions. Here, fifteen candidate models are compared, combining three mechanisms of apoptotic material uptake with five forms of the macrophage death rate. Model comparison is guided by the Akaike Information Criterion and qualitative agreement with the observed lipid distributions. Numerical solutions show that whole-cell uptake models predict internal maxima that are absent from the data, whereas nibbling uptake produces distributions that are too concentrated about their means. By contrast, intermediate "fragmented" uptake models provide substantially improved agreement when combined with either linear lipid-dependent or exponential time-dependent death rates. The fitted models predict that smaller fragments from dying cells are ingested at higher frequency than larger ones. This analysis provides new insight into how efferocytosis shapes the distribution of lipid within macrophage populations and highlights the importance of distribution-level data for distinguishing between mechanistic models that reproduce similar population-average dynamics.

q-bio.CB

SpCAST enables scalable and interpretable integration of single-cell RNA sequencing and single-cell-resolved spatial transcriptomics

Single-cell-resolution spatial transcriptomics (scST) preserves tissue architecture but often provides targeted or sparse transcriptomic measurements, whereas scRNA-seq offers broader coverage without spatial context. We present SpCAST, a scalable and interpretable framework that uses scRNA-seq references to transfer cell identity, reconstruct expression and expose gene-level decision evidence in scST. SpCAST jointly learns reference-cell classification, reference--query alignment and query reconstruction in mini-batches, avoiding the need for a global reference-by-query correspondence matrix. Spatially Aware Gene Attribution (SAGA) approximates the learned decision function with a sparse additive Kolmogorov--Arnold network. Across 53 sections comprising 413,404 spatial cells from five technologies, SpCAST achieved the highest aggregate annotation rank among seven methods and scaled to ten million simulated cells. Controlled masking recovered cell-type-associated expression signals and improved spatial marker concordance. SAGA further resolved expression-dependent gene evidence and distinguished evidence retained or attenuated across intra- and cross-species reference settings.

q-bio.CB