Biology subjects

Levin, M.

Publications and source records attributed to Levin, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Integrative Analysis of Left Ventricle and Epicardial Adipose Tissue Identifies SDHA and OGDH as Candidate Targets for Ischemic Heart Disease

Ischemic heart disease (IHD) involves coordinated molecular changes across heart, yet their interplay remains poorly understood. Here, we investigated transcriptomic alterations in two heart tissue subtypes, left ventricle (LV) and epicardial adipose tissue (EAT), from age- and BMI-matched healthy and IHD individuals, including both diabetic and non-diabetic patients. We performed transcriptomic profiling and systems-level network analysis to identify disease-associated gene expression changes. Our analysis revealed: (1) stronger transcriptional responses in EAT compared to LV, particularly in diabetic individuals, and (2) widespread dysregulation of inflammatory and metabolic pathways, including oxidative phosphorylation, cytokine signaling, and fatty acid degradation, across both tissue subtypes. Co-expression network analysis uncovered shared gene modules, with SDHA (Succinate dehydrogenase complex, subunit A) and OGDH (Oxoglutarate Dehydrogenase) emerging as central, downregulated genes linked to mitochondrial function and inflammation, important processes in IHD pathophysiology. These findings were validated in independent human and mouse datasets. Overall, our integrative analysis identifies conserved molecular signatures across cardiac tissue subtypes, suggesting potential for therapeutic exploration in IHD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/676232v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@7e17eeorg.highwire.dtl.DTLVardef@552321org.highwire.dtl.DTLVardef@1755f30org.highwire.dtl.DTLVardef@bc5872_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

AI-Guided Resetting of Memories in Gene Regulatory Network Models: biomedical and evolutionary implications

Molecular pathways such as gene-regulatory networks regulate numerous functions in cells and tissues that impact embryonic development, regenerative repair, aging, cancer, and many other aspects of health and disease. One important aspect of such networks is experience-dependent plasticity: their activity changes after repeated exposures to external and internal physiological stimuli. This kind of functional plasticity gives rise to habituation to pharmacological interventions (resulting in loss of efficacy over time), sensitization (resulting in unacceptable side effects after repeated use), or canalizing of undesired dynamics that recur even when the original problem has been resolved (persistent disease states). Our in silico analyses reveal that memories formed in gene regulatory networks can be erased by specific further experiences without any changes of network topology (leaving the connectivity in place). We present a method for discovery of stimuli that can be used to selectively delete physiological memories, which can be used to remove unwanted behaviors in biomedical and bioengineering contexts without gene therapy or genomic editing. Remarkably, not only are the training-induced gains in causal emergence not lost after stimuli that wipe memories, but we also find a positive relationship between the causal emergence and learning ability of a network, suggesting a deep asymmetry (ratchet) in the relationship between learning/forgetting and integration of collective intelligence which may have implications for evolution. Significance StatementWe present an AI-driven method for discovering signals that can be used to induce physiological networks to forget specific behaviors, which can be used for applications in biomedicine and bioengineering.

biophysics↗

bioRxiv2025

Triple-Negative Breast Cancer Cells Activate Sensory Neurons via TRPV1 to Drive Nerve Outgrowth and Tumor Progression

The tumor microenvironment in triple-negative breast cancer (TNBC) is characterized by increased sensory nerve density, which contributes to cancer progression by promoting migration and metastasis. However, the origin of tumor-innervating nerves and the mechanisms driving sensory innervation into tumors remain poorly understood. Using in vivo retrograde labeling techniques, we show that mammary tumors are associated with an increase in nerves originating from the dorsal root ganglia. Additionally, we find that TNBC cells trigger stress and activation markers and induce neuronal firing in a transient receptor potential anilloid subtype 1 (TRPV1)-dependent manner. In both 2D and 3D cell culture models, TNBC cells promote outgrowth of sensory nerves that is abrogated with Trpv1 knockout. We identified c-Jun and IL-6 as an effector of neurite outgrowth that acts downstream of TRPV1 to promote outgrowth in vitro. Finally, in Trpv1 knockout mice, TNBC tumors exhibit delayed growth and reduced lung metastasis. These findings suggest that a critical role for TRPV1 in tumor-nerve crosstalk, offering a potential target to reduce metastatic disease.

cancer biology↗

bioRxiv2025

Self-Organizing Neural Networks in Novel Moving Bodies: Anatomical, Behavioral, and Transcriptional Characterization of a Living Construct with a Nervous System

A great deal is known about the formation and architecture of biological neural networks in animal models, which have arrived at their current structure-function relationship through evolution by natural selection. Little is known about the development of such structure-function relationships in a scenario where neurons are allowed to grow within evolutionarily-novel, motile bodies. Previous work showed that when a piece of ectodermal tissue is excised from Xenopus embryos and allowed to develop ex vivo, it will develop into a three-dimensional (3D) mucociliary organoid, and exhibits behaviors different from those observed in tadpoles of the same age. These biological robots or biobots are autonomous, self-powered, and able to move through aqueous environments. Here we report a novel type of biobot that is composed of ciliated epidermis and additionally incorporates neural tissue (neurobots). We show that neural precursor cells implanted within the Xenopus skin constructs develop into mature neurons and extend processes towards the outer surface of the bot as well as among each other. These self-organized neurobots show distinct external morphology, generate more complex patterns of spontaneous movements, and are differentially affected by neuroactive drugs compared to their non-neuronal counterparts. Calcium imaging experiments show that neurons within neurobots are indeed active. Transcriptomics analysis of the neurobots reveals increased variability of transcript profiles, expression of a plethora of genes relating to nervous system development and function, a shift toward more ancient genes, and up-regulation of neuronal genes implicated in visual perception.

bioengineering↗

bioRxiv2025

Epigenetic Control of TERRA by FTSJ3 is Critical for Telomerase-Driven Cancers

Telomerase reverse transcriptase (hTERT) overexpression, a hallmark of most cancers, drives tumorigenesis by enabling limitless replicative potential. Direct targeting of hTERT is challenging, necessitating alternative strategies. Through genome-wide synthetic dosage lethality (SDL) screening in cancer models, including patient-derived organoids, we identify FTSJ3, an RNA 2-O-methyltransferase, as a critical vulnerability in hTERT-overexpressing cells. FTSJ3 methylates telomeric repeat-containing RNA (TERRA), a modification essential for recruiting SUV39H1 to telomeric ends to mediate H3K9 trimethylation and establish stable heterochromatin. Loss of FTSJ3 disrupts this cascade, impairing H3K9 trimethylation, HP1-alpha recruitment, and telomeric heterochromatin maintenance. Notably, this reveals an unexpected dependency on TERRA methylation for telomeric heterochromatin stability in hTERT-driven cancers. Non-malignant cells, lacking telomerase activity and de novo telomere repeat synthesis, are unaffected by FTSJ3 suppression. Our findings establish the FTSJ3/TERRA/SUV39H1 axis as a critical mechanism supporting telomeric heterochromatin stability in hTERT-driven cancers. This telomere-directed epigenetic strategy provides a robust framework for translational therapeutic innovation.

cancer biology↗

bioRxiv2024

Identification of brain-like functional information architectures in embryonic tissue of Xenopus laevis.

Understanding how populations of cells collectively coordinate activity to produce the complex structures and behaviors that characterize multicellular organisms, and which coordinated activities, if any, survive processes that reshape cells and tissues into organoids, are fundamental issues in modern biology. Here we show how techniques from complex systems and multivariate information theory provide a framework for inferring the structure of collective organization in non-neural tissue. Many of these techniques were developed in the context of theoretical neuroscience, where these statistics have been found to be altered during different cognitive, clinical, or behavioral states, and are generally thought to be informative about the underlying dynamics linking biology to cognition. Here we show that these same patterns of coordinated activity are also present in the aneural tissues of evolutionarily distant biological systems: preparations of embryonic Xeno-pus laevis tissue (known as "basal Xenobots"). These similarities suggest that such patterns of activity either arose independently in these two systems (epithelial constructs and brains); are epiphenomenological byproducts of other dynamics conserved across vastly different configurations of life; or somehow directly support adaptive behavior across diverse living systems. Finally, these results provide unambiguous support for the hypothesis that, despite their apparent simplicity as collections of non-neural epithelial cells, Xenobots are in fact integrated, complex systems in their own right, with sophisticated internal information structures.

systems biology↗

bioRxiv2024

Revealing non-trivial information structures in aneural biological tissues via functional connectivity

A central challenge in the progression of a variety of open questions in biology, such as morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Information-theoretic approaches provide a quantitative framework for extracting patterns from data, but so far have been predominantly applied to neuronal systems at the tissue-level. Here, we demonstrate how time series of Ca2+ dynamics can be used to identify the structure and information dynamics of other biological tissues. To this end, we expressed the calcium reporter GCaMP6s in an organoid system of explanted amphibian epidermis derived from the African clawed frog Xenopus laevis, and imaged calcium activity pre- and post- a puncture injury, for six replicate organoids. We constructed functional connectivity networks by computing mutual information between cells from time series derived using medical imaging techniques to track intracellular Ca2+. We analyzed network properties including degree distribution, spatial embedding, and modular structure. We find organoid networks exhibit more connectivity than null models, with high degree hubs and mesoscale community structure with spatial clustering. Utilizing functional connectivity networks, we show the tissue retains non-random features after injury, displays long range correlations and structure, and non-trivial clustering that is not necessarily spatially dependent. Our results suggest increased integration after injury, possible cellular coordination in response to injury, and some type of generative structure of the anatomy. While we study Ca2+ in Xenopus epidermal cells, our computational approach and analyses highlight how methods developed to analyze functional connectivity in neuronal tissues can be generalized to any tissue and fluorescent signal type. Our framework therefore provides a bridge between neuroscience and more basal modes of information processing. Author summaryA central challenge in understanding several diverse processes in biology, including morphogenesis, wound healing, and development, is learning from empirical data how information is integrated to support tissue-level function and behavior. Significant progress in understanding information integration has occurred in neuroscience via the use of observable live calcium reporters throughout neural tissues. However, these same techniques have seen limited use in the non-neural tissues of multicellular organisms despite similarities in tissue communication. Here we utilize methods designed for neural tissues and modify them to work on any tissue type, demonstrating how non-neural tissues also contain non-random and potentially meaningful structures to be gleaned from information theoretic approaches. In the case of epidermal tissue derived from developing amphibians, we find non-trivial informational structure over greater spatial and temporal scales than those found in neural tissue. This hints at how more exploration into information structures within these tissue types could provide a deeper understanding into information processing within living systems beyond the nervous system.

systems biology↗

bioRxiv2024

Natural Induction: Spontaneous adaptive organisation without natural selection

Evolution by natural selection is believed to be the only possible source of spontaneous adaptive organisation in the natural world. This places strict limits on the kinds of systems that can exhibit adaptation spontaneously, i.e. without design. Physical systems can show some properties relevant to adaptation without natural selection or design. 1) The relaxation, or local energy minimisation, of a physical system constitutes a natural form of optimisation insomuch as it finds locally optimal solutions to the frustrated forces acting on it or between its components. 2) When internal structure gives way or accommodates to a pattern of forcing on a system this constitutes learning insomuch as it can store, recall and generalise past configurations. Both these effects are quite natural and general, but in themselves insufficient to constitute non-trivial adaptation. However, here we show that the recurrent interaction of physical optimisation and physical learning together results in significant spontaneous adaptive organisation. We call this adaptation by natural induction. The effect occurs in dynamical systems described by a network of viscoelastic connections subject to occasional disturbances. When the internal structure of such a system accommodates slowly across many disturbances and relaxations, it spontaneously learns to preferentially visit solutions of increasingly greater quality (exceptionally low energy). We show that adaptation by natural induction thus produces network organisations that improve problem-solving competency with experience. We note that the conditions for adaptation by natural induction, and its adaptive competency, are different from those of natural selection. We therefore suggest that natural selection is not the only possible source of spontaneous adaptive organisation in the natural world.

evolutionary biology↗

bioRxiv2024

An ability to respond begins with inner alignment: How phase synchronisation effects transitions to higher levels of agency

How do multiple active components at one level of organisation create agential wholes at higher levels of organisation? For example, in organismic development, how does the multi-scale autonomy of the organism arise from the interactions of the molecules, cells and tissues that an organism contains? And, in the major evolutionary transitions, how does a multicellular organism, for example, arise as an evolutionary unit from the selective interests of its unicellular ancestors? We utilise computational models as a way to think about this general question. We take a deliberately minimalistic notion of an agent: a competency to take one of two possible actions to minimise stress. Helping ourselves to this behaviour at the microscale, we focus on conditions where this same type of agency appears spontaneously at a higher level of organisation. We find that a simple process of positive feedback on the timing of individual responses, loosely analogous to the natural phase synchronisation of weakly coupled oscillators, causes such a transition in behaviour. The emergent collectives that arise become, quite suddenly, able to respond to their external stresses in the same (minimal) sense as the original microscale units. This effects a dramatic rescaling of the system behaviour, and a quantifiable increase in problem-solving competency, serving as a model of how higher-level agency emerges from a pool of lower-level agents or active matter. We discuss how this dynamical waking-up of higher-level collectives, through the alignment of their internal dynamics, might relate to reproductive/cell-cycle synchronisation in evolutionary transitions and development.

evolutionary biology↗

bioRxiv2023

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations

Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models may misrepresent or provide less accurate treatment recommendations for underrepresented populations, contributing to health disparities. This study introduces an adaptable machine learning toolkit that integrates multiple existing methodologies and novel techniques to enhance the prediction accuracy for underrepresented populations in genomic datasets. By leveraging machine learning techniques, including gradient boosting and automated methods, coupled with novel population-conditional re-sampling techniques, our method significantly improves the phenotypic prediction from single nucleotide polymorphism (SNP) data for diverse populations. We evaluate our approach using the UK Biobank, which is composed primarily of British individuals with European ancestry, and a minority representation of groups with Asian and African ancestry. Performance metrics demonstrate substantial improvements in phenotype prediction for underrepresented groups, achieving prediction accuracy comparable to that of the majority group. This approach represents a significant step towards improving prediction accuracy amidst current dataset diversity challenges. By integrating a tailored pipeline, our approach fosters more equitable validity and utility of statistical genetics methods, paving the way for more inclusive models and outcomes.

bioinformatics↗

bioRxiv2023

Programmable delivery of fluoxetine via wearable bioelectronics for wound healing in vivo

The ability to deliver drugs with precise dosages at specific time points can significantly improve disease treatment while reducing side effects. Drug encapsulation for gradual delivery has opened up the doors for superior treatment regimen. To expand on this ability, programming bioelectronic devices to deliver small molecules enables ad-hoc personalized therapeutic profiles that are more complex than simple gradual release. Here, we introduce a wearable bioelectronic bandage with an integrated electrophoretic ion pump that affords on-demand drug delivery with precise dose control. Delivery of fluoxetine to wounds in mice resulted in a 27.2% decrease in the macrophage ratio (M1/M2) and a 39.9% increase in re-epithelialization, indicating a shorter inflammatory phase and faster overall healing. Programmable drug delivery using wearable bioelectronics in wounds introduces a broadly applicable strategy for the long-term delivery of a prescribed treatment regimen with minimal external intervention.

bioengineering↗

bioRxiv2023

Competitive Amplification Networks enable molecular pattern recognition with PCR

Gene expression has great potential to be used as a clinical diagnostic tool. However, despite the progress in identifying these gene expression signatures, clinical translation has been hampered by a lack of purpose-built. readily deployable testing platforms. We have developed Competitive Amplification Networks. CANs to enable analysis of an entire gene expression signature in a single PCR reaction. CANs consist of natural and synthetic amplicons that compete for shared primers during amplification, forming a reaction network that leverages the molecular machinery of PCR. These reaction components are tuned such that the final fluorescent signal from the assay is exactly calibrated to the conclusion of a statistical model. In essence, the reaction acts as a biological computer, simultaneously detecting the RNA targets, interpreting their level in the context of the gene expression signature, and aggregating their contributions to the final diagnosis. We illustrate the clinical validity of this technique, demonstrating perfect diagnostic agreement with the gold-standard approach of measuring each gene independently. Crucially, CAN assays are compatible with existing qPCR instruments and workflows. CANs hold the potential to enable rapid deployment and massive scalability of gene expression analysis to clinical laboratories around the world, in highly developed and low-resource J settings alike. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/546934v3_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@79d1c5org.highwire.dtl.DTLVardef@1bafb87org.highwire.dtl.DTLVardef@d78e31org.highwire.dtl.DTLVardef@1b85125_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Identification of a pharmaceutical biostasis inducer that slows metabolism in multiple vertebrates that do not hibernate

Drugs that induce reversible slowing of metabolic and physiological processes would have great value for organ preservation, especially for organs with high susceptibility to hypoxia-reperfusion injury, such as the heart. Using whole-organism screening of metabolism, mobility, and development in Xenopus, we identified an existing drug, SNC80, that rapidly and reversibly slows biochemical and metabolic activities while preserving cell and tissue viability. Although SNC80 was developed as a delta opioid receptor activator, we discovered that its ability to slow metabolism is independent of its opioid modulating activity as a novel SNC80 analog (WB3) with almost 1,000 times less delta opioid receptor binding activity is equally active. Metabolic suppression was also achieved using SNC80 in microfluidic human organs-on-chips, as well as in explanted whole porcine hearts and limbs, demonstrating the cross-species relevance of this approach and potential clinical relevance for surgical transplantation. Pharmacological induction of physiological slowing in combination with organ perfusion transport systems may offer a new therapeutic approach for tissue and organ preservation for transplantation, trauma management, and enhancing patient survival in remote and low-resource locations.

bioengineering↗

bioRxiv2023

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce Multi-response Mendelian randomization (MR2), a novel MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and viceversa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.

genetics↗

bioRxiv2023

Information integration during bioelectric regulation of morphogenesis in the embryonic frog brain

Spatiotemporal bioelectric states regulate multiple aspects of embryogenesis. A key open question concerns how specific multicellular voltage potential patterns differentially activate distinct downstream genes required for organogenesis. To understand the information processing mechanisms underlying the relationship between spatial bioelectric patterns, genetics, and morphology, we focused on a specific spatiotemporal bioelectric pattern in the Xenopus ectoderm that regulates embryonic brain patterning. We used machine learning to design a minimal but scalable bioelectric-genetic dynamical network model of embryonic brain morphogenesis that qualitatively recapitulated previous experimental observations. A causal integration analysis of the model revealed a simple higher-order spatiotemporal information integration mechanism relating the spatial bioelectric and gene activity patterns, where the latter is expressed as a function of the causal influence of the voltages of groups of cells. Specific aspects of this mechanism include causal apportioning (certain cell positions are more important for collective decision making), informational asymmetry (depolarized cells are more influential than hyperpolarized cells), long distance influence (genes in a cell are variably sensitive to voltage of faraway cells), and division of labor (different genes are sensitive to different aspects of voltage pattern). The asymmetric information-processing character of the mechanism led the model to predict an unexpected degree of plasticity and robustness in the bioelectric prepattern that regulates normal embryonic brain development. Our in vivo experiments verified these predictions via molecular manipulations in Xenopus embryos. This work shows the power of using a minimal in silico approach to drastically reduce the parameter space in vivo, making hard biological questions tractable. These results provide insight into the collective decision-making process of cells in interpreting bioelectric pattens that guide large-scale morphogenesis, suggesting novel applications for biomedical interventions and new tools for synthetic bioengineering.

developmental biology↗

bioRxiv2022

ZNF524 directly interacts with telomeric DNA and supports telomere integrity

Telomeres are nucleoprotein structures at the ends of linear chromosomes. In humans, they consist of TTAGGG repeats, which are bound by dedicated proteins such as the shelterin complex. This complex blocks unwanted DNA damage repair at telomeres, e.g. by suppressing non-homologous end joining (NHEJ) through its subunit TRF2. We here describe ZNF524, a zinc finger protein that directly binds telomeric repeats with nanomolar affinity and reveal the base-specific sequence recognition by co-crystallization with telomeric DNA. ZNF524 localizes to telomeres and specifically maintains the presence of the TRF2/RAP1 subcomplex at telomeres without affecting other shelterin members. Loss of ZNF524 concomitantly results in an increase in DNA damage signaling and recombination events. Overall, ZNF524 is a direct telomere-binding protein involved in the maintenance of telomere integrity.

cell biology↗

bioRxiv2022

Exploring The Behavior of Bioelectric Circuits using Evolution Heuristic Search

Characteristic spatial differences of cellular resting potential across tissues have been shown to act as instructive bioelectric prepatterns regulating embryonic and regenerative morphogenesis, as well as cancer suppression. Indeed, modulation of bioelectric patterns via specific ion channel-targeting drugs, channel misexpression, or optogenetics has been used to control growth and form in vitro, showing promise in regenerative medicine and synthetic bioengineering. Repair of defects, injury, and transformation requires quantitative understanding of bioelectric dynamics within tissues so that these can be modulated toward desired outcomes in organ patterning or the creation of entirely novel synthetic constructs. The major gap in the discovery of interventions for rational control of organ-level outcomes is the inability to predict large-scale bioelectric patterns - their emergence from symmetry breaking (given a set of channels expressed on the tissue) and their change as a function of time under specific bioelectrical interventions. It is thus essential to develop machine learning and other computational tools to help human scientists identify bioelectric states with desirable properties. Here, we tested the ability of a heuristic search algorithm to explore the parameter space of bio-electrical circuits by adjusting the parameters of simulated cells. We show that while bioelectrical space is not easy to search, it does contain parameter sets that encode rich and interesting patterning behaviors. We demonstrate proof of principle of using a computational search platform to identify circuits with desired properties, as a first step toward the design of machine learning tools for improved bioelectric control of growth and form.

biophysics↗

bioRxiv2022

Probing Local Force Propagation in Tensed Fibrous Gels

Fibrous hydrogels are a key component of soft animal tissues. They support cellular functions and facilitate efficient mechanical communication between cells. Due to their nonlinear mechanical properties, fibrous materials display non-trivial force propagation at the microscale, that is enhanced compared to that of linear-elastic materials. In the body, tissues are constantly subjected to external loads that tense or compress them, modifying their micro-mechanical properties into an anisotropic state. However, it is unknown how force propagation is modified by this isotropic-to-anisotropic transition. Here, we directly measure force propagation in tensed fibrin hydrogels. Local perturbations are induced by oscillating microspheres using optical tweezers. We use both 1-point and 2-point microrheology to simultaneously measure both the shear modulus and force propagation. We suggest a mathematical framework to quantify anisotropic force propagation trends. We show that force propagation becomes anisotropic in tensed gels, with, surprisingly, stronger response to perturbations perpendicular to the axis of tension. Our results suggest that under external loads, there are favoured directions of mechanical communication between cells in a tissue. Importantly, we also find that external tension increases the range of force transmission by altering the power-law exponent governing the decay of oscillations with distance from the perturbation. We end with a discussion of possible implications and future directions for research.

biophysics↗