Biology subjects

Murugan, A.

Publications and source records attributed to Murugan, A..

14 recordsLinked to original sources

bioRxiv2026

A DNA-barcoded Luria-Delbrück assay resolves mechanisms of adaptation

When populations encounter a harsh new environment, they adapt in many different ways - from spontaneous genetic changes to transient non-genetic plasticity. Distinguishing between mechanisms of adaptation is often difficult, as the key events are rare and hard to observe directly. Here, we introduce HiDenSeq, a Luria-Delbruck assay that uses DNA barcoding and deep sequencing to resolve the Luria-Delbruck distribution - the statistics of adaptation to a new environment - over four orders of magnitude. At this statistical depth, the shape of the distribution encodes the underlying adaptive mechanism. We find that DNA mismatch repair mutants shift the distributions scale without changing its shape, indicating their only effect is as mutator alleles. A pulse of UV mutagenesis, however, adds a second, statistically distinguishable mode atop the spontaneous background, showing that different mechanisms of adaptation can be quantified directly from the distribution. By sampling rare-event statistics with DNA barcodes, HiDenSeq provides a general method for studying mechanisms of adaptation in evolving populations from microbes to cancers.

evolutionary biology↗

bioRxiv2025

Trainable computation in molecular networks

Reports of learning in single cells without genetic change span decades yet remain controversial, in part because there is no accepted general molecular mechanism for training comparable to gradient-based training or Hebbian learning in neural circuits. Here we identify a minimal set of ingredients sufficient to realize non-genetic learning, drawing inspiration from Boltzmann neural networks. First, dense reversible interaction networks provide an expressive substrate in which modulating the concentrations of a small set of mediator species can reprogram function without altering the underlying interaction parameters. Second, a simple rate-sensitive autoregulatory scheme that adjusts these mediator levels provides a local Hebbian-like training rule that can train the same network for diverse tasks, including Pavlovian conditioning, supervised classification, and generative tuning of bet-hedging ratios to match environmental statistics. We show that this autoregulatory training rule is model free and applies to reversible multimerization networks of arbitrary complexity, so training can compensate for unknown or unmodeled interactions present in vivo. These results suggest design principles for trainable synthetic cellular circuits and indicate how molecular systems could learn statistical features of their environments through experience.

systems biology↗

bioRxiv2025

Ecosystems as adaptive living circuits

Unlike many physical nonequilibrium systems, in biological systems, the coupling to external energy sources is not a fixed parameter but adaptively controlled by the system itself. We do not have theoretical frameworks that allow for such adaptability. As a result, we cannot understand emergent behavior in living systems where structure formation and non-equilibrium drive coevolve. Here, using ecosystems as a model of adaptive systems, we develop a framework of living circuits whose architecture changes adaptively with the energy dissipated in each circuit edge. We find that unlike traditional nonequilibrium systems, living circuits exhibit a phase transition from equilibrium death to a nonequilibrium dissipative state beyond a critical driving potential. This transition emerges through a feedback mechanism that saves the weakest edges by routing dissipation through them, even though the adaptive rule locally rewards the strongest dissipating edges. Despite lacking any global optimization principle, living circuits achieve near-maximal dissipation, with higher drive promoting more complex circuits. Our work establishes ecosystems as paradigmatic examples of living circuits whose structure and dissipation are tuned through local adaptive rules.

ecology↗

bioRxiv2025

Microbial functional guilds respond cohesively to rapidly fluctuating environments

Microbial communities experience environmental fluctuations across timescales from rapid changes in moisture, temperature, or light levels to long-term seasonal or climactic variations. Understanding how microbial populations respond to these changes is critical for predicting the impact of perturbations, interventions, and climate change on communities. Since communities typically harbor tens to hundreds of distinct taxa, the response of microbial abundances to perturbations is potentially complex. However, while taxonomic diversity is high, in many communities taxa can be grouped into functional guilds of strains with similar metabolic traits. These guilds effectively reduce the complexity of the system by providing a physiologically motivated coarse-graining. Here, using a combination of simulations, theory, and experiments, we show that the response of guilds to nutrient fluctuations depends on the timescale of those fluctuations. Rapid changes in nutrient levels drive cohesive, positively correlated abundance dynamics within guilds. For slower timescales of environmental variation, members within a guild begin to compete due to similar resource preferences, driving negative correlations in abundances between members of the same guild. Our results provide a route to understanding the relationship between functional guilds and community response to changing environments, as well as an experimental approach to discovering functional guilds via designed nutrient perturbations to communities.

biophysics↗

bioRxiv2024

Microglia undergoes chemokine receptor, CX3CR1-mediated internalization of extracellular Tau

In Alzheimers disease, the microtubule-associated protein, Tau misfolds to form aggregates and filaments in the intra- and extracellular region of neuronal cells. CX3CR1 receptor is involved in monomeric Tau internalization by directly binding to the receptor. In our study, we are interested in understanding the interaction of different pathological structures of Tau with microglial chemokine receptor, CX3CR1 and its role in CX3CR1-mediated Tau internalization by immunofluorescence assay. Co-immunoprecipitation further confirmed the receptor interaction with Tau oligomers and even aggregated filaments. We next determined the role of Tau as a chemoattractant in regulating the migration of microglia by wound healing assay. Further, immunolocalization studies of CX3CR1 with {beta}-arrestin proved the desensitization of CX3CR1 receptor upon Tau binding. Upon ligand interaction, GPCRs undergoes desensitization and encytosed to the cytosol. We have quantified the CX3CR1 internalization by measuring the membrane to cytosol intensity ratio of CX3CR1 receptor upon extracellular Tau exposure. In addition to experimental assays, a comprehensive analysis of the interaction between the CX3CR1 receptor, the Tau protein, and the AZD8797 ligand has been derived from the combined molecular docking, molecular dynamics and free energy calculations using molecular mechanics-generalized Born approach. In particular, two different molecular dynamics simulations were carried out for CX3CR1-Tau protein and AZD8797-bound CX3CR1-Tau protein interaction. The molecular modeling study establishes that AZD8797 binds to CX3CR1 receptor and modulates the Tau interaction with the receptor.

cell biology↗

bioRxiv2024

Direct and indirect selection in a proofreading polymerase

The traits that affect evolvability are subject to indirect selection, as these traits affect the course of evolution over many generations rather than the direct replicative fitness of an individual. However, the evolution of evolvability-determining traits is often difficult to study because putative evolvability alleles often have confounding direct fitness effects of unknown origin and size. Here, we study theoretically and experimentally the evolution of mutation rates in proofreading polymerases with orthogonal control of direct and indirect selection. Mutagenic DNA polymerases enjoy a long-time fitness advantage by enhancing the rate of acquiring beneficial mutations. However, this is offset by a short-time fitness penalty, which we trace to a counterintuitive trade-off between mutation rates and activity in proofreading polymerases. Since these fitness effects act on different timescales, no one number characterizes the fitness of a mutator allele. We find unusual dynamic features in the resulting evolutionary dynamics, such as kinetic exclusion, selection by dynamic environments, and Rock-Paper-Scissors dynamics in the absence of ecology. Our work has implications for the evolution of mutation rates and more broadly, evolution in the context of an anti-correlation between mutation rates and short term fitness.

evolutionary biology↗

bioRxiv2024

Metavinculin mediates differentiation cues into myogenic gene expression.

Isoform switching in deciphering differentiation signals into myogenic transcriptional output remains unexplored. Here, we report a distinct function of muscle-specific metavinculin isoform in switching canonical to non-canonical Wnt-pathways to establish skeletal muscle differentiation. Metavinculin expression is specifically associated with muscle differentiation, regeneration and absent in proliferating myoblasts. During differentiation, metavinculin-specific exon retention is facilitated by the direct binding of Rbfox1 to its downstream intron. Depletion of metavinculin impairs skeletal muscle differentiation, while its ectopic expression induces genes associated with differentiation even in proliferating myoblasts. Subsequent results revealed that pharmacological activation of canonical Wnt impedes muscle differentiation, whereas its inhibition stimulates differentiation. Canonical Wnt inhibition or ectopic non-canonical Wnt7b expression restores muscle differentiation programs in metavinculin-depleted cells. These results indicates a dynamic interplay between Rbfox1-generated metavinculin and Wnt-signaling pathways. This advancement sheds light on the intricate molecular mechanisms underlying skeletal muscle differentiation and suggests potential therapeutic targets for muscle-related disorders.

cell biology↗

bioRxiv2024

Dynamic coexistence driven by physiological transitions in microbial communities

AO_SCPLOWBSTRACTC_SCPLOWMicrobial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes often modify their environments so strongly that they are forced out of the exponential state into stressed or non-growing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory. Here, we introduce a phenomenological model, the Community State model, to gain insight into the dynamic coexistence of microbes due to changes in their physiological states. Our model bypasses specific interactions (e.g., nutrient starvation, stress, aggregation) that lead to different combinations of physiological states, referred to collectively as "community states", and modeled by specifying the growth preference of each species along a global ecological coordinate, taken here to be the total community biomass density. We identify three key features of such dynamical communities that contrast starkly with steady-state communities: increased tolerance of community diversity to fast growth rates of species dominating different community states, enhanced community stability through staggered dominance of different species in different community states, and increased requirement on growth dominance for the inclusion of late-growing species. These features, derived explicitly for simplified models, are proposed here to be principles aiding the understanding of complex dynamical communities. Our model shifts the focus of ecosystem dynamics from bottom-up studies based on idealized inter-species interaction to top-down studies based on accessible macroscopic observables such as growth rates and total biomass density, enabling quantitative examination of community-wide characteristics.

ecology↗

bioRxiv2023

Closed ecosystems extract energy through self-organized nutrient cycles

Our planet is a self-sustaining ecosystem powered by light energy from the sun, but roughly closed to matter. Many ecosystems on Earth are also approximately closed to matter and recycle nutrients by self-organizing stable nutrient cycles, e.g., microbial mats, lakes, open ocean gyres. However, existing ecological models do not exhibit the self-organization and dynamical stability widely observed in such planetary-scale ecosystems. Here, we advance a new conceptual model that explains the self-organization, stability and emergent features of closed microbial ecosystems. Our model incorporates the bioenergetics of metabolism into an ecological framework. By studying this model, we uncover a crucial thermodynamic feedback loop that enables metabolically diverse communities to almost always stabilize nutrient cycles. Surprisingly, highly diverse communities self-organize to extract{approx} 10% of the maximum extractable energy, or{approx} 100 fold more than randomized communities. Further, with increasing diversity, distinct ecosystems show strongly correlated fluxes through nutrient cycles. However, as the driving force from light increases, the fluxes of nutrient cycles become more variable and species-dependent. Our results highlight that self-organization promotes the efficiency and stability of complex ecosystems at extracting energy from the environment, even in the absence of any centralized coordination.

ecology↗

bioRxiv2022

Tissue confinement regulates cell growth and size in epithelia

Cell proliferation is a central process in tissue development, homeostasis and disease. Yet how proliferation is regulated in the tissue context remains poorly understood. Here, we introduce a quantitative framework to elucidate how tissue growth dynamics regulate cell proliferation. We show that tissue growth causes confinement that suppresses cell growth; however, this confinement does not directly affect the cell cycle. This leads to uncoupling between rates of cell growth and division in epithelia and, thereby, reduces cell size. Division becomes arrested at a minimal cell size, which is consistent across diverse epithelia in vivo. Here, the nucleus approaches a volume limit set by the compacted genome. The loss of Cyclin D1-dependent cell size regulation results in an abnormally high nuclear-to-cytoplasmic volume ratio and DNA damage. Overall, we demonstrate how epithelial proliferation is regulated by the interplay between tissue confinement and cell size regulation. Highlights- In epithelia, regulation of cell growth and cycle are uncoupled - Cell growth is regulated by tissue-scale dynamics, which determine confinement - Cell volume in epithelial tissue is described by G1 sizer model with a tunable growth rate - Volume of cells in epithelial tissues is near a minimum set by genome size

biophysics↗

bioRxiv2020

Ligand-receptor promiscuity enables cellular addressing

In multicellular organisms, secreted ligands selectively activate, or "address," specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, each defined by its receptor expression profile. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capacity. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increasing receptor multiplicity, and is maximized by specific biochemical parameter relationships. Together, these results identify fundamental design principles governing cell addressing by ligand combinations.

systems biology↗

bioRxiv2020

Proofreading through spatial gradients

Key enzymatic processes in biology use the nonequilibrium error correction mechanism called kinetic proofreading to enhance their specificity. Kinetic proofreading typically requires several dedicated structural features in the enzyme, such as a nucleotide hydrolysis site and multiple enzyme-substrate conformations that delay product formation. Such requirements limit the applicability and the adaptability of traditional proofreading schemes. Here, we explore an alternative conceptual mechanism of error correction that achieves delays between substrate binding and subsequent product formation by having these events occur at distinct physical locations. The time taken by the enzyme-substrate complex to diffuse from one location to another is leveraged to discard wrong substrates. This mechanism does not require dedicated structural elements on the enzyme, making it easier to overlook in experiments but also making proofreading tunable on the fly. We discuss how tuning the length scales of enzyme or substrate concentration gradients changes the fidelity, speed and energy dissipation, and quantify the performance limitations imposed by realistic diffusion and reaction rates in the cell. Our work broadens the applicability of kinetic proofreading and sets the stage for the study of spatial gradients as a possible route to specificity.

biophysics↗

bioRxiv2019

Input dose differentiation by NF-κB

Cells receive a wide range of dynamic signaling inputs during immune regulation, but how gene regulatory networks measure and interpret such dynamic inputs is not understood. Here, we used microfluidic live-cell analysis and mathematical modeling to study how NF-{kappa}B pathway in single-cells responds to time-varying immune inputs such as increasing, decreasing or fluctuating cytokine signals. Surprisingly, we found that NF-{kappa}B acts as a differentiator, responding strictly to the absolute difference in cytokine concentration, and not to the concentration itself. Our analyses revealed that negative feedbacks by the regulatory proteins A20 and I{kappa}B enable dose differentiation by providing short-term memory of prior cytokine level and continuously resetting kinase cycling and receptor levels. Investigation of NF-{kappa}B target gene expression showed that cells create unique transcriptional responses under different dynamic cytokine profiles. Our results demonstrate how cells use simple network motifs and transcription factor dynamics to efficiently extract information from complex signaling environments.

systems biology↗

bioRxiv2018

Information content of downwelling skylight for non-imaging visual systems

Light-sensitive proteins (opsins) are expressed in non-imaging tissues like the brain, dermis and reproductive organs of most animals. Such tissues have been shown to sense the intensity and spectrum of light over time. Functional links to circadian and reproductive rhythms have been speculated but remain uncertain. Here we use information theory to quantify the natural scene for non-imaging opsins, i.e., spectral patterns in downwelling skylight. Our approach synthesizes measurements of natural downwelling spectra, atmospheric distortions, and weather, with the biophysical constraints of opsins and biochemical clocks, while minimizing assumptions about how organisms process such information. We find that tissues expressing multiple opsins could use twilight to extract significant information about lunar phase and time of day in many climates. In contrast, information in light intensity is far less robust to atmospheric perturbations. Thus our work quantifies circalunar and circadian regularities in the spectrum of downwelling radiance salient to non-imaging opsins.

biophysics↗