Biology subjects

Johnson, C. G.

Publications and source records attributed to Johnson, C. G..

7 recordsLinked to original sources

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

Hydroplane I: one-shot probabilistic evolutionary analysis for scalable organizational identification.

Applying evolutionary genomics to microbial and viral community sequence information presents significant challenges. Metagenomic sequences are typically stored in large databases of short-read fragments with unknown relationships. Available tools for analysis are often slow, rely on incomplete subsets of data, or focus on narrowly defined sub-problems. Moreover, existing tools often depend on simplistic model assumptions or treat inferences as empirical data, which can distort downstream analyses. In this work, we present theory and initial validation of the first stage of a fast, one-shot Bayesian approach to metagenomic evolutionary analysis. Our primary result here demonstrates effective use of simple models in experimental design using collections of short proximal short oligonucleotide sequences (kmers) to detect probable homologs, and validates the speed, sensitivity and specificity of this approach in a test set of whole Prochlorococcus genome sequences. Significance StatementThe Hydroplane algorithm provides an efficient framework for analyzing co-evolutionary relationships among microbial and viral genomes using metagenomic data. Hydroplane guides the identification of homologous regions, estimates lineage diversity, and reveals evolutionary events such as mutation, divergence, selection, and horizontal gene transfer. Its computationally lightweight design supports scalable genome clustering and adaptation analyses, enabling the study of rare microbial and viral genomes across diverse ecological contexts. With an accessible implementation, Hydroplane broadens the scope of evolutionary genomics research, offering critical insights into host-pathogen dynamics and supporting biopreparedness through the study of microbial and viral genetic relationships.

evolutionary biology↗

bioRxiv2025

Genotype-phenotype modeling of light ecotypes in Prochlorococcusreveals genomic signatures of ecotypic divergence

Prochlorococcus species are the most abundant marine photosynthetic bacteria. Despite broadly shared phenotypic traits and marine habitats, they exhibit remarkable genomic diversity. We ask what genomic signatures underlie its ecotypic divergence into high- and low-light adapted lineages, and whether these signatures can still be recovered from incomplete assemblies. From [~]1,000 publicly available Prochlorococcus genomes, we focused on those with information on their light adaptation ecotype (high-light/low-light), phylogenetic clades, and depth of isolation. Across these divisions, we calculated average nucleotide identity and constructed pangenomes to assess cyanobacterial core genes vs. those that separate ecotypes. Despite scant conservation, we observe a sharp taxon separation by light ecotypes. Classical machine learning models trained to predict ecotype achieve near-perfect binary classification accuracy even when predicting on partial genomes (Matthews Correlation Coefficient = 0.86 - 1.00), while regression models trained to predict the depth of isolation performed poorly, with high root mean square error values (37.6 - 42.0m). For ecotype prediction, we analyzed top gene features across model runs and classes; these features included photosynthesis-associated genes and pathways, as well as many novel markers of unknown function. When separating ecotypes further by previously described phylogenetic clades, genomic content and composition show even clearer separation among clades, supporting the taxonomic breadth of the Prochlorococcus collective. These results emphasize the genomic specialization underlying ecotypic divergence and support the utility of ML approaches for cyanobacterial ecotype prediction from metagenomic data. Expanded sampling will yield novel clade-specific biology. All data, models, and results are available on GitHub: https://github.com/JRaviLab/cyano_adaptation. ImportanceProchlorococcus are common aquatic cyanobacteria that can derive energy from light. They can be classified into high-/low-light ecotypes depending on how they use light. Prochlorococcus have small genomes compared to other bacteria, but the gene sets they carry are also remarkably flexible, which may help them survive and adapt to their harsh oceanic environment. We studied hundreds of Prochlorococcus genomes from around the world in an effort to predict ecotypes from partial genome sequences. We used comparative genomics, machine learning, and other statistical methods to identify genomic features associated with ecotypes. These statistical approaches predicted ecotypes accurately, reliably, and according to large differences in gene content and genome structure. Our results support that Prochlorococcus can be divided into different species or genera based on clades, and provide many gene targets for further research to understand cyanobacterial circadian rhythms or improve their bioengineering potential as chassis organisms.

bioinformatics↗

bioRxiv2025

Phenotypic scoring of Canola Blackleg severity using machine learning image analysis

Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola (Brassica napus L., Brassica rapa L., Brassica juncea L.) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties. Consequently, scoring blackleg disease severity of infected plants is a key metric for identifying and selecting resistant varieties. Traditionally, blackleg severity is scored by expert raters who evaluate disease in stem cross sections using established rating scales and reference images; however, human raters are expensive and inconsistent in their scoring. Here, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross-section images of infected plants. We find that expert ratings are largely inconsistent across raters and across years for the same rater, creating substantial noise in susceptibility ratings. Meanwhile, our trained machine learning model performs more consistently than the median rater while maintaining a similar heritability as expert raters for the blackleg susceptibility trait. This model can be used to standardize blackleg susceptibility scoring across locations and years to improve canola breeding outcomes across affected regions. Core IdeasO_LICanola Blackleg is a fungal disease affecting yield of canola, and accurate scoring of Blackleg severity is important for tracking disease and breeding for resistant varieties. C_LIO_LIThe standard practice of utilizing expert raters is expensive, and scores assigned are inconsistent across raters and years. C_LIO_LIOur deep learning model for assigning blackleg severity scores is more accurate than the median expert rater, opening the door for improved breeding of new resistant varieties. C_LI

plant biology↗

bioRxiv2024

Synergy and antagonism in a genome-scale model of metabolic hijacking by bacteriophage

Bacteriophage auxiliary metabolic genes (AMGs) alter host metabolism by hijacking reactions. Previous studies used functional annotations to infer AMG impacts but neglected propagation effects on global metabolism and phage production. We demonstrate the first integration of AMGs and phage assembly into a genome-scale metabolic model, using a general method applied to the infection of Prochloroccocus marinus MED4 by P-HM2. We experimentally validate our approach to predicting AMG impact on growth using cp12 mutations in Syne-chococcus elongatus. We predict that 17 directly hijacked reactions substantially impact over 30% of the metabolism, including carbon fixation, photosynthesis, and nucleotide synthesis. We find that indirect impacts are synergistically and antagonistically coupled and are either phage-aligned--shifting feasible reaction velocities in accordance with maximal phage production--or phage-antialigned. Pareto optimization reveals that phage-aligned reactions limit host growth, while phage-antialigned reactions do not. We provide systems-level insight into AMG perturbations, highlighting how nontrivial cascading effects shape microbial functions.

systems biology↗

bioRxiv2024

Toll-Like-Receptor 5 protects against pulmonary fibrosis by reducing lung dysbiosis

Idiopathic pulmonary fibrosis (IPF) is a devastating pulmonary disease with no curative treatment other than lung transplantation. IPF results from maladaptive responses to lung epithelial injury, but the underlying mechanisms remain unclear. Here, we show that deficiency in the innate immune receptor, toll-like receptor 5 (TLR5), is associated with IPF in humans and with increased susceptibility to epithelial injury and experimental fibrosis in mice, while activation of lung epithelial TLR5 through a synthetic flagellin analogue protects from experimental fibrosis. Mechanistically, epithelial TLR5 activation induces antimicrobial gene expression and ameliorates dysbiosis after lung injury. In contrast, TLR5 deficiency in mice and IPF patients is associated with lung dysbiosis. Elimination of the microbiome in mice through antibiotics abolishes the protective effect of TLR5 and reconstitution of the microbiome rescues the observed phenotype. In aggregate, TLR5 deficiency is associated with IPF and dysbiosis in humans and in the murine model of pulmonary fibrosis. Furthermore, TLR5 protects against pulmonary fibrosis in mice and this protection is mediated by effects on the microbiome. One-sentence summaryDeficiency in the innate immune receptor TLR5 is a risk factor for pulmonary fibrosis, because TLR5 prevents microbial dysbiosis after lung injury.

immunology↗

bioRxiv2017

Nestin in immature embryonic neurons regulates axon growth cone morphology and Sema3a sensitivity

Correct wiring in the neocortex requires that responses to an individual guidance cue vary among neurons in the same location, and within the same neuron over time. Nestin is an atypical intermediate filament expressed highly in neural progenitors and is thus used widely as a progenitor marker. Here we show a subpopulation of embryonic cortical neurons which transiently express nestin in their axons. Nestin expression is thus not restricted to neural progenitors but persists at lower levels in some newborn neurons for 2-3 days. We found that nestin-expressing neurons have smaller growth cones, suggesting that nestin affects cytoskeletal dynamics. Nestin, unlike other intermediate filament subtypes, regulates cdk5 kinase. Cdk5 activity is induced by the repulsive guidance cue Sema3a leading to growth cone collapse in vitro. Therefore, we tested whether nestin-expressing neurons showed altered responses to Sema3a. We find that nestin-expressing newborn neurons are more sensitive to Sema3a in a roscovitine-sensitive manner, whereas nestin knockdown results in lowered sensitivity to Sema3a. We propose that nestin functions in immature neurons to modulate cdk5 and thereby the Sema3a response. Thus, the transient expression of nestin could allow for temporal modulation of a neuron's response to Sema3a particularly during early axon guidance decisions.

cell biology↗