Biology subjects

Igoshin, O. A.

Publications and source records attributed to Igoshin, O. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrating cell-state and enhancer constraints discriminates models of PU.1 regulation during hematopoietic differentiation

Distinct PU.1 levels are associated with progenitor, B-cell, and macrophage states during hematopoietic differentiation. A previous dynamical-systems model attributed these levels to multistability generated by PU.1 autoregulation coupled to feedback between PU.1 abundance and cell-cycle progression, but its predictions were not quantitatively evaluated against the motivating measurements. Here, we test this mechanism using reported measurements of PU.1 abundance, its synthesis rate, and cell-cycle duration, and develop progressively more mechanistic alternatives. The original growth-feedback model neither represents the two reported progenitor groups nor reproduces the nonmonotonic relationship between PU.1 abundance and cell-cycle length. Even with experimentally measured cell-type-specific growth rates, PU.1 autoregulation alone does not reproduce the observed PU.1 abundance and synthesis rates across cell states. Reproducing the cell-state measurements requires an additional cell-state-dependent input to PU.1 production. Guided by enhancer regulation, we model positive feedback between PU.1 and C/EBP and constrain it with reporter measurements, but this architecture cannot jointly reproduce the reporter and cell-state datasets. Adding RUNX1 permits data-consistent stable states when high effective cooperativity in PU.1-dependent C/EBP activation is allowed. Thus, combined dynamical and cis-regulatory constraints support additional regulation of PU.1 production beyond autoregulation, cell-type-specific dilution, and C/EBP feedback. The RUNX1-extended architecture provides one biologically grounded realization and generates experimentally testable hypotheses for differentiation networks.

biophysics↗

bioRxiv2025

Biophysical Modeling Elucidates Mechanistic Principles for Rational Molecular Glue Design

Molecular glues are small molecules that offer a powerful strategy to target previously "undruggable" proteins of interest (POI) by enhancing their interactions with other proteins (effector). Depending on the nature of the effectors, molecular glues can induce stabilizing sequestration or degradation of POI. However, their rational design has been hindered by a poor understanding of how kinetic parameters impact their performance. To address this, we developed a unified mathematical framework that accurately analyzes the dynamics of both glue degraders and stabilizers. Our model reveals that the strong binding affinity of the ternary complex is the key determinant of performance across both modalities, provided that the initial component concentrations and degradation rate constants are fixed. Furthermore, we demonstrate that degrader performance is ultimately limited by its catalytic efficiency and the target proteins natural half-life. We also identify distinct roles for effector abundance, showing that the relative concentrations of the effector and POI are critical for stabilizers but less so for degraders. This quantitative framework provides mechanistic principles for the rationale design and optimization of molecular glues.

molecular biology↗

bioRxiv2025

A Deep Learning Framework for Quantifying Dynamic Self-Organization in Myxococcus xanthus

Under starvation, Myxococcus xanthus bacteria initiate a multicellular developmental program in which cells move to form fruiting bodies and differentiate into distinct cell types. Many genes affecting this process have been identified, and it is assumed that perturbing genes within the same pathway induces similar changes in the phenotype, although those changes may be subtle or obscured by pleiotropic effects. However, these pathways cannot be systematically mapped, as there are no systematic methods for quantifying phenotype similarity. Here, we applied deep learning techniques to quantify the phenotype patterns and self-organization dynamics of 292 genetically distinct strains. By integrating ResNet and StyleGAN2 to construct a Variational AutoEncoder (VAE) and utilizing a Siamese network for phenotypic similarity metrics, we efficiently encoded high-resolution microscopy images into 13-dimensional feature vectors, capturing phenotypic variability in aggregation patterns across time and strains. Human evaluation confirmed that our models reconstructions were visually indistinguishable from real images and closely aligned with input phenotypes. Importantly, the feature space is interpretable: individual dimensions correlate with biological features such as aggregate number and size, and extrapolation along these dimensions produces predictable morphological changes. Remarkably, our model revealed that developmental phenotypes are predictable from the earliest stages before visible aggregate formation begins. This predictability held across both genetic and environmental sources of variation, suggesting fundamental constraints on developmental trajectories and indicating that subtle phenotypic variations carry important information. These results demonstrate how machine learning can reveal hidden structure in complex multicellular dynamics and provide scalable methods for phenotypic analysis without manual annotation.

systems biology↗

bioRxiv2025

Modeling σE biochemical network reveals context-dependent feedback control and kinetic constraints in the envelope stress response

The bacterial cell envelope is essential for mechanical stability, barrier function, defining cell size and shape, and supporting cellular processes. Because external or internal stressors can challenge its integrity, bacteria have evolved stress response systems to maintain envelope homeostasis. For example, under stress, E. coli activates {sigma}E to induce a regulon that restores envelope homeostasis. The molecular mechanisms underlying the transcriptional and post-translational regulation of {sigma}E and its anti-sigma factor RseA have been well-mapped. However, how these regulatory layers function at the systems level to regulate {sigma}E activity remains unclear. Here, we combine mathematical modeling with quantitative gene expression measurements to determine how the interplay of transcription and post-translational regulation affects the dynamics of the {sigma}E response. The results suggest that the {sigma}E activity is determined by the balance between the release from RseA, resulting from its degradation, and slow binding of free {sigma}E to the intact RseA. Notably, the combined action of transcriptional and post-translational regulation reveals that autoregulation, traditionally assumed to be a positive feedback, transitions from negative to positive feedback under extreme stress, enabling a finely tuned response that prevents premature activation while ensuring a robust response under severe envelope stress. In summary, our findings elucidate the mechanisms of {sigma}E regulation, thereby advancing our understanding of how alternative sigma factor bacterial networks control stress-response pathways.

systems biology↗

bioRxiv2025

Effects of chemical modulators on enzyme specificity

Chemical inhibitors bind to enzymes, thereby inhibiting their catalytic activity. While many enzymes catalyze reactions with a single substrate, others, like DNA polymerase, can act on multiple related substrates. Substrate-selective inhibitors (SSIs) target these multi-substrate enzymes to modulate their specificity. Although SSIs hold promise as therapeutics, our theoretical understanding of how different inhibitors influence enzyme specificity remains limited. In this study, we examine enzyme selectivity within kinetic networks corresponding to known inhibition mechanisms. We demonstrate that competitive and uncompetitive inhibitors do not affect substrate specificity, regardless of rate constants. In contrast, noncompetitive and mixed inhibition can alter specificity and can lead to non-monotonic responses to the inhibitor. We show that mixed and non-competitive inhibitors achieve substrate-selective inhibition by altering the effective free-energy barriers of product formation pathways that are enabled by the inhibitors presence. We then apply this framework to the Sirtuin-family deacylase SIRT2, showing that the suicide inhibitor thiomyristoyl lysine (TM) cannot influence substrate specificity unless there is a direct substrate exchange reaction or biochemical constraints are relaxed. These findings provide insights into engineering systems where cofactor binding modulates metabolic flux ratios.

biophysics↗

bioRxiv2025

Factors Influencing Monoclonal Antibody Pharmacokinetics Across Varying Immune Perturbations

The development of continuous-release devices or injectables for the long-term delivery of biologics is of great interest, especially monoclonal antibodies (mAbs) that require frequent, high-dose injections. Preclinical testing of these technologies in murine models is necessary for clinical translation; however, xenogeneic responses to the mAb and foreign body responses to the implants or injectables can confound results. Immune system knockout (KO) models that affect immune cells are often used in these experiments, but the effects of KO models on mAb pharmacokinetics (PK) are not well characterized. Here, we investigated the PK profile of the human mAb 3BNC117 after intravenous, subcutaneous, and intraperitoneal injections in four mouse strains: BL6, BCD, RAG2, and NSG mice. Noncompartmental analysis was used to quantify differences in PK between each mouse strain. Strikingly, both BL6 and NSG mice exhibited significantly higher mAb clearance compared to the other two strains. To better understand these differences, we developed a minimally physiological based PK model of mAb PK and estimated model parameters using nonlinear mixed effects modeling. The fitted model parameters illustrated how specific processes change in each strain, including the change in clearance rates over time in BL6 and differences in mAb lymphatic uptake in NSG mice. We then used simple allometric scaling relationships to assess which strains were reasonably predictive of human mAb PK. NSG and BL6 mice were found to be unpredictive of human PK, unlike RAG2 and BCD mice. Overall, these results highlight the importance of selecting an appropriate KO strain for preclinical mAb evaluation and demonstrate that RAG2 and BCD strains are suitable mouse models for investigation of mAb PK. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/651349v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@138af32org.highwire.dtl.DTLVardef@763196org.highwire.dtl.DTLVardef@1b17331org.highwire.dtl.DTLVardef@10199e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Kinetic mechanisms for the sequence dependence of transcriptional errors

The fidelity of template-dependent mRNA synthesis during transcription elongation is the primary determinant of accurate gene expression and the maintenance of functional RNA transcripts. However, the mechanisms governing transcription fidelity remain incompletely understood. While previous studies have characterized how error rates vary with nucleotide identity at upstream and downstream positions from the incorporation site, the comprehensive microscopic explanation of this sequence dependence has not been elucidated. In this study, we develop a novel theoretical approach that integrates transcription proofreading mechanisms and inhomogenous DNA sequence effects. Using first-passage analysis validated by Monte Carlo simulations, we quantitatively characterize nucleotide-specific error rates during RNA polymerase II transcription. The model accurately reproduces experimental error rates and predicts kinetic parameters influencing transcriptional fidelity. Analysis reveals nucleotide incorporation rates follow the hierarchy U<C<G<A, consistent with independent experimental observations. Notably, our model not only explains how the error rates depend on the nature of the base immediately down-stream (+1) but also predicts that the identity of the nucleotide at the second downstream position (+2) also plays an important role. Pyrimidines at position +2 contribute to lower error rates than purines, whereas the third downstream base (+3) has no effect. These previously unreported correlations are corroborated by bioinformatic analysis of existing datasets. In addition, using the BRCA1 gene as an example, we explore the physiological implications of sequence-dependent error rates, identifying an increased likelihood of premature stop codon errors. These findings clarify how DNA sequence context modulates nucleotide incorporation kinetics, advancing our understanding of transcriptional fidelity and its functional consequences.

biophysics↗

bioRxiv2025

The growth benefits and toxicity of quinone synthesis are balanced by a dual regulatory mechanism and substrate limitations

Quinones play a central role in maintaining redox balance and conserving energy but can trigger oxidative stress at high levels. However, the mechanisms by which microbes regulate quinone levels remain poorly0020understood, hindering effective metabolic engineering to modulate microbes for quinone production. Here, we show that the synthesis of the menaquinone precursor DHNA in the lactic acid bacterium Lactococcus lactis is regulated by a combined genetic, enzymatic, and metabolic mechanism. Using synthetic biology approaches, we found that enzymes MenF and MenD both contribute to DHNA regulation, with MenD playing a more prominent role in controlling DHNA concentrations. A mathematical model elucidates a two-phase regulatory pattern resulting from the interplay of reversible flux and allosteric feedback inhibition, where either MenF or MenD can serve as the regulatory enzyme, depending on their relative expression ratio. Additionally, the overproduction of DHNA is constrained by substrate availability, ensuring a sufficient but not excessive DHNA level to benefit cell growth while mitigating cytotoxicity. Collectively, these mechanisms maintain a fine-tuned physiological quinone level and suggest that modulating substrate supplement and MenF-to-MenD ratio could be keys for engineering DHNA production

systems biology↗

bioRxiv2025

Changes in Spo0A~P pulsing frequency control biofilm matrix deactivation

Under starvation conditions, B. subtilis survives by differentiating into one of two cell types: biofilm matrix-producing cells or sporulating cells. These two cell-differentiation pathways are activated by the same phosphorylated transcription factor - Spo0A~P. Despite sharing the activation mechanism, these cell fates are mutually exclusive at the single-cell level. This decision has been shown to be controlled by the effects of growth rate on gene dosage and protein dilution in the biofilm matrix production network. In this work, we explore an alternative mechanism of growth rate-mediated control of this cell fate decision. Namely, using deterministic and stochastic modeling, we investigate how the growth-rate-dependent pulsing dynamics of Spo0A~P affect biofilm matrix deactivation and activation. Specifically, we show that the Spo0A~P pulsing frequency tunes the biofilm matrix deactivation probability without affecting the probability of biofilm matrix activation. Interestingly, we found that DNA replication is the cell cycle stage that most substantially contributes to the deactivation of biofilm matrix production. Finally, we report that the deactivation of biofilm matrix production is not primarily regulated by the effects of growth rate on gene dosage and protein dilution. Instead, it is driven by changes in the pulsing period of Spo0A~P. In summary, our findings elucidate the mechanism governing biofilm deactivation during the late stages of starvation, thereby advancing our understanding of how bacterial networks interpret dynamic transcriptional regulatory signals to control stress-response pathways. Author SummaryBacteria have evolved various adaptation mechanisms to survive under challenging environmental conditions. For instance, under mild starvation, B. subtilis bacteria form biofilms -- communities of cells encapsulated in a protective extracellular matrix. On the other hand, these bacteria differentiate into highly resistant spores under severe starvation. Interestingly, sporulating cells are often found within biofilm communities, but they do not contribute to biofilm matrix production. This is thought to be an energy-conservation strategy, as biofilm formation is an energy-intensive process and is therefore halted before sporulation begins. Though previous work has focused on the mechanisms driving biofilm disassembly, few studies have explored the regulatory processes that B. subtilis employs to halt matrix production prior to starting sporulation. In this study, we use mathematical models to demonstrate that the temporal dynamics of the biofilm master regulator Spo0A~P control the deactivation of matrix production. Understanding the regulation of biofilm, a common lifestyle in bacteria, can lead to the development of synthetic strategies to either enhance or disrupt biofilm formation, with potential applications in medicine and industry.

systems biology↗

bioRxiv2025

Kinetic mechanism for fidelity of CRISPR-Cas9 variants

CRISPR-Cas9 is a nuclease creating DNA breaks at sites with sufficient complementarity to the RNA guide. Notably, Cas9 does not require exact RNA-DNA complementarity and can cleave off-target sequences. Various high-accuracy Cas9 variants have been developed, but the precise mechanism of how these variants achieve higher accuracy remains unclear. Here, we develop a kinetic model of Cas9 substrate selection and cleavage. We parameterize the model using datasets available in the literature, including both high-throughput substrate binding and cleavage data and Forster resonance energy transfer measurements of the Cas9 HNH domain transitions. Based on the observed transition statistics, we predict that the Cas9 substrate recognition and cleavage mechanism must allow for HNH domain transitions independent of substrate binding. Additionally, we show that the enhancement in Cas9 substrate specificity must be due to changes in kinetics rather than changes in substrate binding affinities. Furthermore, the fitted model produces quantitatively realistic cleavage error predictions for substrates with protospacer adjacent motif (PAM)-distal mismatches. Finally, we use our model to identify kinetic parameters for HNH domain transitions that can be perturbed to enable high-accuracy cleavage while maintaining cleavage speeds. Our results refine the biophysical mechanism of Cas9 cleavage to inform future routes for its engineering.

biophysics↗

bioRxiv2025

Biophysical modeling reveals the transcriptional regulatory mechanism of Spo0A, the master regulator in starving Bacillus subtilis

In starving Bacillus subtilis bacteria, the initiation of two survival programs - biofilm formation and sporulation - is both controlled by the same phosphorylated master regulator Spo0A[~]P. Its gene, spo0A, is transcribed from two promoters, Pv and Ps, that are respectively regulated by RNAP holoenzymes bearing {sigma}A and {sigma}H. Notably, transcription is directly autoregulated by Spo0A[~]P binding sites known as 0A1, 0A2, and 0A3 box, located in between the two promoters. It remains unclear whether, at the onset of starvation, these boxes activate or repress spo0A expression, and whether the Spo0A[~]P transcriptional feedback plays a role in the increase in spo0A expression. Based on the experimental data of the promoter activities under systematic perturbation of the promoter architecture, we developed a biophysical model of transcription regulation of spo0A by Spo0A[~]P binding to each of the 0A boxes. The model predicts that Spo0A[~]P binding to its boxes does not affect the RNA polymerase recruitment to the promoters but instead affects the transcriptional initiation rate. Moreover, the effects of Spo0A[~]P binding to 0A boxes are mainly repressive and saturated early at the onset of starvation. Therefore, the increase in spo0A expression is mainly driven by the increase in RNAP holoenzyme levels. Additionally, we reveal that Spo0A[~]P affinity to 0A boxes is strongest at 0A3 and weakest at 0A2 and that there are attractive forces between the occupied 0A boxes. Our findings, in addition to clarifying how the sporulation master regulator is controlled, offer a framework to predict regulatory outcomes of complex gene-regulatory mechanisms. ImportanceCell differentiation is often critical for survival. In bacteria, differentiation decisions are controlled by transcriptional master regulators under transcriptional feedback control. Therefore, understanding how master regulators are transcriptionally regulated is required to understand differentiation. However, in many cases, underlying regulation is complex with multiple transcription factor binding sites and multiple promoters, making it challenging to dissect the exact mechanisms. Here, we address this problem for the B. subtilis master regulator Spo0A. Using a biophysical model, we quantitatively characterize the effect of individual transcription factor binding sites on each spo0A promoter. Further, the model allows to identify the specific transcription step that is affected by transcription factor binding. Such model is promising for the quantitative study of a wide range of master regulators involved in transcriptional feedback.

biophysics↗

bioRxiv2024

Genetic and Environmental Determinants of Streaming and Aggregation in Myxococcus xanthus

Under starvation conditions, a spot of a few million Myxococcus xanthus cells on agar will migrate inward to form aggregates that mature into dome-shaped fruiting bodies. This migration is thought to occur within structures called streams, which are considered crucial for initiating aggregation. The prevailing traffic jam model hypothesizes that intersections of streams cause cell crowding and jamming, thereby initiating the process of aggregate formation. However, this hypothesis has not been rigorously tested, in part due to the lack of a standardized, quantifiable definition of streams. To address this gap, we captured time-lapse movies and conducted fluorescent cell tracking experiments using wild-type and two motility-deficient mutant M. xanthus strains. By quantitatively defining streams and developing a novel stream detection mask, we show that streams are not essential for nascent aggregate formation, though they may accelerate the process. Moreover, our results indicate that streaming has a genetic component: disrupting only one of the two M. xanthus motility systems hinders stream formation. Together, these findings challenge the idea that stream intersections are required to drive aggregate formation and suggest that M. xanthus aggregation may be driven by mechanisms independent of streaming, highlighting the need for alternative models to fully explain aggregation dynamics.

microbiology↗

bioRxiv2024

Uncovering the Interleukin-12 Pharmacokinetic Desensitization Mechanism and Its Consequences with Mathematical Modeling

The cytokine interleukin-12 (IL-12) is a potential immunotherapy because of its ability to induce a Th1 immune response. However, success in the clinic has been limited due to a phenomenon called IL-12 desensitization - the trend where repeated exposure to IL-12 leads to reduced IL-12 concentrations (pharmacokinetics) and biological effects (pharmacodynamics). Here, we investigated IL-12 pharmacokinetic desensitization via a modeling approach to (i) validate proposed mechanisms in literature and (ii) develop a mathematical model capable of predicting IL-12 pharmacokinetic desensitization. Two potential causes of IL-12 pharmacokinetic desensitization were identified: increased clearance or reduced bioavailability of IL-12 following repeated doses. Increased IL-12 clearance was previously proposed to occur due to the upregulation of IL-12 receptor on T-cells that causes increased receptor-mediated clearance in the serum. However, our model with this mechanism, the accelerated-clearance model, failed to capture trends in clinical trial data. Alternatively, our novel reduced-bioavailability model assumed that upregulation of IL-12 receptor on T-cells in the lymphatic system leads to IL-12 sequestration, inhibiting the transport to the blood. This model accurately fits IL-12 pharmacokinetic data from three clinical trials, supporting its biological relevance. Using this model, we analyzed the model parameter space to illustrate that IL-12 desensitization occurs over a robust range of parameter values and to identify the conditions required for desensitization. We next simulated local, continuous IL-12 delivery and identified several methods to mitigate systemic IL-12 exposure. Ultimately, our results provide quantitative validation of our proposed mechanism and allow for accurate prediction of IL-12 pharmacokinetics over repeated doses.

bioengineering↗

bioRxiv2024

Beyond microtubules: The cellular environment at the endoplasmic reticulum attracts proteins to the nucleus, enabling nuclear transport

All proteins are translated in the cytoplasm, yet many, including transcription factors, play vital roles in the nucleus. While previous research has concentrated on molecular motors for the transport of these proteins to the nucleus, recent observations reveal perinuclear accumulation even in the absence of an energy source, hinting at alternative mechanisms. Here, we propose that structural properties of the cellular environment, specifically the endoplasmic reticulum (ER), can promote molecular transport to the perinucleus without requiring additional energy expenditure. Specifically, physical interaction between proteins and the ER impedes their diffusion and leads to their accumulation near the nucleus. This result explains why larger proteins, more frequently interacting with the ER membrane, tend to accumulate at the perinucleus. Interestingly, such diffusion in a heterogeneous environment follows Chapmans law rather than the popular Ficks law. Our findings suggest a novel protein transport mechanism arising solely from characteristics of the intracellular environment. HighlightsO_LIThe interaction of proteins with ER slows down their diffusion at the perinucleus. C_LIO_LIThis leads proteins to migrate toward the perinucleus without ATP consumption. C_LIO_LIFrequent ER interaction of larger proteins promotes perinuclear accumulation. C_LIO_LIDiffusion with the physical interaction can be described by Chapmans law. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/575351v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@c9f052org.highwire.dtl.DTLVardef@1c0cc36org.highwire.dtl.DTLVardef@9f182forg.highwire.dtl.DTLVardef@ecc143_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Cell behaviors underlying Myxococcus xanthus aggregate coarsening

The soil bacterium Myxococcus xanthus is a model organism with a set of diverse behaviors. These behaviors include the starvation-induced multicellular development program, in which cells move collectively to assemble multicellular aggregates. After initial aggregates have formed, some will disperse, with smaller aggregates having a higher chance of dispersal. Initial aggregation is driven by two changes in cell behavior: cells slow down inside of aggregates and bias their motion by reversing direction less frequently when moving towards aggregates. However, the cell behaviors that drive dispersal are unknown. Here we use fluorescent microscopy to quantify changes in cell behavior after initial aggregates have formed. We observe that after initial aggregate formation, cells adjust the bias in reversal timings by initiating reversals more rapidly when approaching unstable aggregates. Using agent-based modeling, we then show dispersal is predominantly generated by this change in bias, which is strong enough to overcome slowdown inside aggregates. Notably, the change in reversal bias is correlated with the nearest aggregates size, connecting cellular activity to previously observed correlations between aggregate size and fate. To determine if this connection is consistent across strains, we analyze a second M. xanthus strain with reduced levels of dispersal. We find that far fewer cells near smaller aggregates modified their bias. This implies that aggregate dispersal is under genetic control, providing a foundation for further investigations into the role it plays in the life cycle of M. xanthus. ImportanceUnderstanding the processes behind bacterial biofilm formation, maintenance, and dispersal is essential for addressing their effects on health and ecology. Within these multicellular communities, various cues can trigger differentiation into distinct cell types, allowing cells to adapt to their specific local environment. The soil bacterium Myxococcus xanthus forms biofilms in response to starvation, marked by cells aggregating into mounds. Some aggregates persist as spore-filled fruiting bodies, while others disperse after initial formation for unknown reasons. Here, we use a combination of cell tracking analysis and computational simulations to identify behaviors at the cellular level that contribute to aggregate dispersal. Our results suggest that cells in aggregates actively determine whether to disperse or persist and undergo a transition to sporulation based on a self-produced cue related to the aggregates size. Identifying these cues is an important step in understanding and potentially manipulating bacterial cell-fate decisions.

microbiology↗

bioRxiv2022

The slowdown of growth rate controls the single-cell distribution of biofilm matrix production via a SinI-SinR-SlrR network

In Bacillus subtilis, a master regulator Spo0A controls several cell-differentiation pathways. Under moderate starvation, phosphorylated Spo0A (Spo0A~P) induces biofilm formation by indirectly activating genes controlling matrix production in a subpopulation of cells via a SinI-SinR-SlrR network. Under severe starvation, Spo0A~P induces sporulation by directly and indirectly regulating sporulation gene expression. However, what determines the heterogeneity of individual cell fates is not fully understood. In particular, it is still unclear why, despite being controlled by a single master regulator, biofilm matrix production and sporulation are mutually exclusive on a single-cell level. In this work, with mathematical modeling, we showed that the fluctuations in the growth rate and the intrinsic noise amplified by the bistability in the SinI-SinR-SlrR network could explain the single-cell distribution of matrix production. Moreover, we predicted an incoherent feed-forward loop: the decrease in the cellular growth rate activates matrix production by increasing in Spo0A phosphorylation level but represses it via changing the relative concentrations of SinR and SlrR. Experimental data provide evidence to support model predictions. In particular, we demonstrate how the degree to which matrix production and sporulation appear mutually exclusive is affected by genetic perturbations. ImportanceThe mechanisms of cell-fate decisions are fundamental to our understanding of multicellular organisms and bacterial communities. However, even for the best-studied model systems we still lack a complete picture of how phenotypic heterogeneity of genetically identical cells is controlled. Here, using B. subtilis as a model system, we employ a combination of mathematical modeling and experiments to explain the population-level dynamics and single-cell level heterogeneity of biofilm gene expression. The results demonstrate how the two cell fates, biofilm matrix production and sporulation, can appear mutually exclusive without explicitly inhibiting one another. Such a mechanism could be used in a wide range of other biological systems.

systems biology↗

bioRxiv2021

Quantification of Myxococcus xanthus Aggregation and Rippling Behaviors: Deep-learning Transformation of Phase-contrast into Fluorescence Microscopy Images

Myxococcus xanthus bacteria are a model system for understanding pattern formation and collective cell behaviors. When starving, cells aggregate into fruiting bodies to form metabolically inert spores. During predation, cells self-organize into traveling cell-density waves termed ripples. Both phase-contrast and fluorescence microscopy are used to observe these patterns but each has its limitations. Phase-contrast images have higher contrast, but the resulting image intensities lose their correlation with cell density. The intensities of fluorescence microscopy images, on the other hand, are well correlated with cell density, enabling better segmentation of aggregates and better visualization of streaming patterns in between aggregates. However, fluorescence microscopy requires the engineering of cells to express fluorescent proteins and can be phototoxic to the cells. To combine the advantages of both imaging methodologies, we develop a generative adversarial network that converts phase-contrast into fluorescent images. By including an additional histogram-equalized output to the state-of-art pix2pixHD algorithm, our model generates accurate images of aggregates and streams, enabling the estimation of aggregate positions and sizes, but with small shifts of their boundaries. Further training on ripple patterns enables accurate estimation of the rippling wavelength. Our methods are thus applicable for many other phenotypic behaviors and pattern formation studies.

systems biology↗

bioRxiv2021

Emergent myxobacterial behaviors arise from reversal suppression induced by kin contacts

A wide range of biological systems - from microbial swarms to bird flocks, display emergent behaviors driven by coordinated movement of individuals. To this end, individual organisms interact by recognizing their kin and adjusting their motility based on others around them. However, even in the best-studied systems, the mechanistic basis of the interplay between kin recognition and motility coordination is not understood. Here, using a combination of experiments and mathematical modeling, we uncover the mechanism of an emergent social behavior in Myxococcus xanthus. By overexpressing cell surface adhesins, TraA and TraB, involved in kin recognition, large numbers of cells adhere to one another and form organized macroscopic circular aggregates that spin clockwise or counterclockwise. Mechanistically, TraAB adhesion results in sustained cell-cell contacts that trigger cells to suppress cell reversals, and circular aggregates form as the result of cells ability to follow their own cellular slime trails. Furthermore, our in-silico simulations demonstrate a remarkable ability to predict self-organization patterns when phenotypically distinct strains are mixed. For example, defying naive expectations, both models and experiments found that strains engineered to overexpress different and incompatible traAB allelles nevertheless form mixed circular aggregates. Therefore, this work provides key mechanistic insights into M. xanthus social interactions and demonstrates how local cell contacts induce emergent collective behaviors by millions of cells. ImportanceIn many species, large populations exhibit emergent behaviors whereby all related individuals move in unison. For example, fish in schools can all dart in one direction simultaneously to avoid a predator. Currently, it is impossible to explain how such animals recognize kin through brain cognition and elicit such behaviors at a molecular level. However, microbes also recognize kin and exhibit emergent collective behaviors that are experimentally tractable. Here, using a model social bacterium, we engineer dispersed individuals to organize into synchronized collectives that create emergent patterns. With experimental and mathematical approaches we explain how this occurs at both molecular and population levels. The results demonstrate how the combination of local physical interactions triggers intracellular signaling, which in turn leads to emergent behavior on a population scale.

biophysics↗