Biology subjects

Kim, J. K.

Publications and source records attributed to Kim, J. K..

At least 19 recordsLinked to original sources

bioRxiv2026

A physics-informed neural network framework for inferring infinitesimal phase response curves from finite-pulse circadian recordings

Biological clocks reset and entrain to external cues in a phase-dependent manner. The infinitesimal phase response curve (iPRC) provides the local dynamical rule underlying this process by quantifying how an input changes the instantaneous phase velocity of an oscillator. Inferring iPRCs would make it possible to predict how rhythms respond to new stimulus timings and durations, but existing approaches often require detailed mechanistic models, long perturbation recordings, or specialized population-level resetting experiments. Here we introduce Physics-informed neural network for iPRC inference (PINN-iPRC), a physics-informed neural network framework for inferring iPRCs from standard finite-pulse stimulus-response recordings. PINN-iPRC represents the unknown iPRC as a neural network and embeds the phase equation into the loss function, identifying the iPRC that leads to the observed transient phase trajectory under the known stimulus. In synthetic oscillators, PINN-iPRC accurately recovered iPRCs from finite-pulse recordings, including complex ones that are difficult to capture with simpler parametric reconstructions. Applied to existing experimental Arabidopsis circadian recordings, PINN-iPRC recovered iPRCs comparable to those obtained from specialized population-level resetting experiments, while requiring only conventional stimulus-response trajectories. The inferred iPRCs were further used to predict finite-duration PRCs and circadian peak phases across photoperiods. In particular, the predicted near-invariance of the prr7 peak phase across 8-16 h of darkness was consistent with independently published prr7-11 root recordings. These results support PINN-iPRC as a practical physics-informed approach for extracting interpretable phase-sensitivity functions from routine biological oscillator recordings, with potential applications in experimental design, entrainment prediction, and chronotherapeutic or pharmacological schedule optimization.

bioinformatics↗

bioRxiv2026

Gene duplication of SNAPC1 generates transcription factors for snRNAs and sex-specific piRNAs

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs essential for transposon silencing and germline integrity across metazoans. In many species, piRNA expression is sexually dimorphic, yet the molecular mechanisms underlying this sex specificity remain poorly understood. In Caenorhabditis elegans, sexually dimorphic piRNA expression is regulated at the transcriptional level. We previously identified SNPC-1.3, a paralog of the small nuclear RNA (snRNA) activating protein complex (SNAPc/SNPC) subunit SNAPC1, as a male-specific piRNA transcription factor. However, the factors governing female piRNA expression remained elusive. Here, we identify SNPC-1.2, a second SNPC-1 paralog, as a female-specific piRNA transcription factor. SNPC-1.2 interacts with the core piRNA transcriptional machinery, binds female piRNA loci, is required for female piRNA expression, and promotes hermaphrodite fertility. In contrast, a third paralog, SNPC-1.1, retains the ancestral SNAPc function in snRNA transcription and is dispensable for piRNA biogenesis. Together, these findings reveal how gene duplication and functional specialization within the snpc-1 gene family generate specificity factors that direct the core SNAP complex to distinct genomic targets, providing a molecular mechanism for sexually dimorphic piRNA expression while maintaining canonical snRNA transcription.

molecular biology↗

bioRxiv2026

Adipocyte Expression of O-Glycoprotein Procollagen C-Endopeptidase Enhancer Protein 2 (PCPE2): Mechanisms Linking Fibrosis and Beiging of White Adipose Tissue

BackgroundProcollagen C-endopeptidase enhancer protein 2 (Pcpe2) has been primarily investigated in collagen processing during wound healing and is assumed to function similarly to Pcpe1 in the extracellular matrix (ECM). Our studies suggest that Pcpe2 has unique structural and functional features not shared with Pcpe1. MethodsTo study the role of Pcpe2 in adipose tissue remodeling, we created adipose tissue-specific knockout of Pcpe2 (TgAd+Pcpe2KO) and adipose tissue-specific Pcpe2 overexpressing (TgAd+Pcpe2Ox) mice and performed in vivo and ex vivo experiments. ResultsWe show that TgAd+Pcpe2KO mice are resistant to WD-induced obesity and exhibit reductions in body and fat pad mass. Promethion cage studies showed no significant differences in lean mass or food intake, yet WD-fed TgAd+Pcpe2KO mice exhibited significantly higher energy expenditure than negative Cre littermate controls. WD-fed TgAd+Pcpe2KO mice also showed reduced plasma glucose and lipoprotein lipid concentrations compared to controls. While markers of adipose tissue inflammation were reduced in WD-fed TgAd+Pcpe2KO mice. Examination of mature adipocytes from white adipose tissue showed that WD consumption greatly stimulated Pcpe2 expression, while no change in Pcpe2 expression in the stromal vascular cells was noted. However, in VAT precursor cells (PCs), the CD140b+ population showed a shift from fibroadipogenic precursors towards adipocyte PCs in TgAd+Pcpe2KO mice. This shift in PCs may account for the attenuation of local inflammation and fibrosis in the absence of Pcpe2. Ex vivo differentiation of adipose PCs showed that loss of Pcpe2 enhanced adipocyte differentiation, reducing TGF{beta}-like signaling via pSmad2/3, and increasing mitochondrial function. Furthermore, unlike Pcpe1, Pcpe2 contains a mucin-like linker domain with nine sites of O-linked glycosylation which may regulate receptor signaling at the plasma membrane. ConclusionsOur results show the ECM O-glycoprotein Pcpe2 is a robust marker of unhealthy adipose tissue expansion in humans and mice and contributes to inflammation and fibrosis associated with WD-induced obesity.

cell biology↗

bioRxiv2026

FOXO/DAF-16 modulates the transcription factor ROR/NHR-23 and inhibits the let-7 microRNA to maintain multipotency during dauer

Animals rapidly reprogram gene expression to adapt development to environmental stress. How gene regulatory programs that drive continuous development are repressed during stress-induced developmental arrest remains poorly understood. In Caenorhabditis elegans, starvation and overcrowding trigger entry into the stress-resistant, quiescent dauer stage. Here, we identify interactions among the conserved transcription factors DAF-16/FOXO and NHR-23/ROR, and the let-7 family of microRNAs as key regulators of the switch from continuous development to dauer. We show that loss of daf-16 during dauer causes elevated let-7 family microRNAs and premature expression of the adult collagen reporter col-19p::GFP. Reducing let-7 family activity suppresses this phenotype, whereas dauer-specific let-7 expression is sufficient to induce col-19p::GFP expression. Mechanistically, DAF-16 inhibits let-7 transcription in part by repressing nhr-23, which encodes a transcriptional activator of the let-7 family and molting-cycle genes. ChIP-seq analysis reveals DAF-16 binding upstream of nhr-23, and daf-16; daf-7 mutant dauers exhibit increased nhr-23 mRNA and NHR-23 protein, supporting a model in which DAF-16 directly represses nhr-23. Integrated ChIP-seq and transcriptomic analyses identify 1,183 genes activated and 681 genes repressed by DAF-16 during dauer. Repressed targets are enriched for pro-growth genes involved in mitotic DNA replication and translational elongation. DAF-16 targets include 59 transcription factors that may mediate broader transcriptional reprogramming during dauer to maintain multipotency and establish quiescence. Together, these findings reveal that DAF-16/FOXO establishes stress-induced developmental arrest by coupling activation of protective pathways with repression of conserved developmental timing, growth, and differentiation programs. Significance statementAnimals often pause development during environmental stress and then resume normal development when conditions improve. How developmental programs are temporarily halted without disrupting later cell fates remains poorly understood. We show that, during stress-induced dauer arrest in C. elegans, the conserved FOXO transcription factor DAF-16 represses the differentiation-promoting let-7 microRNA pathway. DAF-16 inhibits the ROR homolog NHR-23, which normally activates let-7 and molting-cycle genes. Genome-wide analyses further show that DAF-16 both activates stress-response genes and represses growth and developmental genes. These findings reveal how a conserved stress-responsive factor coordinates survival, developmental arrest, and maintenance of cellular multipotency.

molecular biology↗

bioRxiv2026

Derivation and theoretical validation of fractional quasi-steady state approximation (fQSSA) for target-mediated drug disposition models with memory effects

Standard target-mediated drug disposition (TMDD) models are widely used to describe nonlinear pharmacokinetics driven by high-affinity drug-target interactions. However, their reliance on instantaneous binding limits their ability to capture delayed and history-dependent dynamics observed in vivo. Here, we introduce a fractional TMDD model that incorporates memory effects through a fractional derivative, thereby generalizing the standard TMDD (sTMDD) framework. Although this fractional TMDD (fTMDD) formulation increases modeling flexibility, it also exacerbates parameter identifiability challenges under typical experimental conditions where only drug concentration data are available. To address this limitation, we derive a fractional quasi-steady-state approximation (fQSSA) that reduces model dimensionality while preserving essential nonlinear and memory-dependent pharmacokinetic dynamics. We further establish an explicit validity condition that quantifies the approximation error of both fTMDD and fQSSA without requiring numerical simulation. This condition reveals that the initial drug-to-target ratio is the primary determinant of QSSA validity, whereas the fractional order has a comparatively minor influence. Application of the proposed framework to recombinant human erythropoietin (rhEPO) data demonstrates that fractional dynamics play a population-dependent role, improving model performance in adults but not in infants. Together, this work provides the first systematic derivation of a QSSA framework for fractional TMDD models, along with rigorous and computable applicability conditions. Our results establish a principled foundation for incorporating memory effects into pharmacokinetic modeling and offer a generalizable framework for nonlinear PK-PD systems involving binding-mediated dynamics. Author summaryMany drugs interact strongly with their biological targets, leading to complex and nonlinear pharmacokinetics that are commonly described using target-mediated drug disposition (TMDD) models. However, these models assume that drug-target interactions occur instantaneously, which limits their ability to capture delayed and history-dependent behaviors observed in real biological systems. In this study, we develop a new modeling framework that incorporates such memory effects by extending TMDD models using fractional calculus. To make the model more practical and computationally efficient, we derive a simplified version based on a quasi-steady-state approximation (QSSA) and provide a clear mathematical condition that determines when this simplification is valid. Our analysis shows that the accuracy of the simplified model is primarily controlled by the initial ratio of drug to target, while the influence of memory effects is comparatively smaller. When applied to experimental data for erythropoietin, our model reveals that memory effects are important in adults but negligible in infants, suggesting that these effects may reflect underlying physiological differences. Overall, this work provides a systematic and interpretable framework for incorporating memory effects into pharmacokinetic modeling, with potential applications to a wide range of drug systems involving complex binding dynamics.

pharmacology and toxicology↗

bioRxiv2026

Real world data based evaluation of a novel target-mediated drug disposition approximation model

Target-mediated drug disposition (TMDD) models have been widely used to describe nonlinear pharmacokinetic profiles driven by high-affinity, low-capacity drug-target binding. A pTMDD model, derived by applying the Pade approximation of the quasi-steady-state (QSS) model (qTMDD) was previously proposed. Although pTMDD model showed a comparable estimation accuracy while maintaining computational efficiency, further validation in realistic clinical scenarios and comprehensive performance evaluations have been needed to assess its practical applicability. Here, we evaluated the pTMDD model using five clinical datasets and extended the previous study that focused on simulations. Using the full TMDD as a reference, the approximation models were compared in terms of the parameter estimation results (parameter estimates, relative standard error values and model diagnostics) and computational efficiency (estimation and bootstrap runtimes). The pTMDD model, previously validated in simulation settings, also preserved the estimation accuracy while reducing the computation time of the clinical data. Both pTMDD and qTMDD remained close to the full TMDD model, whereas Michaelis-Menten TMDD (mTMDD) model showed substantial discrepancies especially at low doses, including biased estimates for key TMDD-related parameters (e.g., kdeg, kint, krec, and kup) and higher objective function values. Moreover, pTMDD was faster than qTMDD in four of the five cases compared to the full TMDD. The time savings were particularly pronounced for larger datasets, supporting the computational efficiency of pTMDD. Q2PCONV, an R Shiny application that converts NONMEM code from qTMDD to pTMDD, was also developed, thereby making this new approximation more accessible to researchers. The findings support pTMDD as a practical alternative to existing TMDD approximation models. Author SummaryTarget-mediated drug disposition (TMDD) models describe a high-affinity, low-capacity binding between drug and its target. To avoid overparameterization, approximation models have been used. The two primary models are Michaelis-Menten model (mTMDD), which is accurate only at high doses, and Quasi-steady-state (qTMDD), which is accurate in wider ranges but requires longer runtime. We have proposed a new approximation model named pTMDD. Here, we evaluated pTMDD using five real clinical trial datasets to assess its practical usefulness. pTMDD produced parameter estimates closer to those from the full TMDD model and showed lower uncertainty than both the full TMDD and mTMDD models. In terms of computational efficiency, pTMDD reduced estimation time by an average of 11% and bootstrap time by an average of 6% relative to qTMDD across cases. In addition, we also developed an R shiny application to help researchers apply pTMDD in practice. Our work supports pTMDD as a practical and efficient tool for TMDD modeling in drug development.

pharmacology and toxicology↗

bioRxiv2026

A Preoptic Neurocircuit That Modulates Metabolic Flexibility

Precise, dynamic control of metabolic fuel usage in response to environmental challenges such as altered food availability or temperature change is essential for animal survival. In mammals, metabolic flexibility--the capacity to shift cellular metabolism between carbohydrate and fatty acid oxidation--is understood to be largely regulated by circulating hormones such as insulin and glucagon. However, the role of the central nervous system in coordinating fuel selection and tissue metabolic tuning remains underexplored. Here, we investigated the mechanisms that mediate metabolic reprogramming following the acute activation of torpor-associated glutamatergic Adcyap1+ torpor-regulating neurons in the anteroventral preoptic area (avPOAVglut2/PACAP). The activation of these neurons rapidly shifts whole-body fuel use from glucose to fatty acids, irrespective of fuel/food availability. This shift is associated with reduced glucose utilization stemming from the transient induction of selective insulin resistance in skeletal muscle. We find that this reduction in skeletal muscle glucose metabolism does not require direct muscle innervation but is rather mediated in part via corticosterone. In contrast to their activation, avPOAVglut2/PACAP neuronal silencing results in improved glucose tolerance, demonstrating powerful bidirectional control of tissue-specific glucose metabolism, whole-body glucose levels, and fuel usage. Together, our findings uncover a novel POA -skeletal muscle pathway that dynamically controls glucose utilization and metabolic flexibility.

neuroscience↗

bioRxiv2026

Exact formula of the total quasi-steady state approximation in competitive target-mediated drug disposition

Competitive target-mediated drug disposition (competitive TMDD) arises when two drugs compete for the same target receptor. These dynamics can be characterized by the full competitive TMDD model; yet, its complexity motivated the use of a reduced model, which is invalid under high receptor concentrations. While this problem can be resolved by using the total quasi-steady state approximation (tQSSA), which remains valid for all receptor conditions, the exact formula of the tQSSA-based reduced model--competitive qTMDD--has remained unknown for 15 years, as it requires solving a cubic equation and the nontrivial task of identifying a biologically meaningful solution. Consequently, researchers have relied on numerical approximation methods, which impose substantial computational costs to maintain accuracy and thereby hinder their practical use in complex real-world applications. To address this problem, we derive--for the first time--a real-valued exact formula for competitive qTMDD by leveraging analytic properties of cubic equations and geometric characteristics of their roots in the complex plane. This exact formula improved computational speed by more than 11-fold compared to previous numerical approximation methods, thereby enabling Bayesian inference using competitive qTMDD, which had been impractical due to excessive computational time. When applied to real-world data from clinical trials, competitive qTMDD estimated pharmacological parameter estimates comparable to those from the full competitive TMDD model while requiring only 30-43% of the computation time. Importantly, this estimation using competitive qTMDD remained consistently accurate regardless of data sparsity, whereas the previous reduced model produced biased estimates under sparse sampling conditions. By ensuring precise biological interpretation of drug systems even in complex real-world scenarios, the exact formula of competitive qTMDD has the potential to significantly streamline the drug development and clinical testing process. Our exact formula also consists entirely of real-valued terms, allowing seamless integration into existing pharmacometrics software. Author SummaryCompetitive target-mediated drug disposition (competitive TMDD) occurs when two drugs compete for the same target receptor. Analyzing this interaction has faced a dilemma for 15 years: choosing between a full model that is accurate but computationally intensive, and a reduced model that is fast but often loses accuracy under real-world clinical conditions. Researchers tried to solve this dilemma by deriving an accurate reduced model; however, it was mathematically challenging. As a result, they had to rely on numerical approximations--which require substantial computational power to maintain the accuracy needed for clinical use, making them impractical in real-world scenarios. In this study, we derive a first-ever exact formula for the new reduced model that is accurate across all biological conditions. This formula computes more than 11 times faster than previous numerical methods, making advanced statistical analyses--such as Bayesian inference--feasible for the first time in this context. When applied to real-world clinical data for Anakinra and rhIL-7-hyFc, our method yielded parameter estimates as accurate as the full model but required significantly less computation time. This breakthrough provides a more accurate biological interpretation and better guidance for determining the right drug dose, potentially accelerating drug development and reducing associated costs.

pharmacology and toxicology↗

bioRxiv2025

Initial Condition Decision to Ensure Reliable Circadian Phase Estimation with Shorter-Term Wearable Data

Various studies in the medical field highlight the importance of circadian medicine, which optimizes treatment timing based on patients circadian phases. While the circadian phase has been measured using dim light melatonin onset (DLMO), the gold standard marker, its high cost and time-intensive nature have led to the development of alternative estimation methods. Among these, the most promising method is using ordinary differential equation (ODE) models, which simulate the circadian rhythm by using a light exposure profile estimated from sleep data. These ODE models require an initial condition (IC) representing the initial state of the circadian rhythm, which is unknown in real-world settings. However, it is unclear how the uncertainty of IC affects the accuracy of circadian phase estimation. In this study, by using sleep data collected from 28 shift workers using ActiWatch (mean duration = 56 days, range = 34-75 days), we found that [≥]18 days of sleep data are required for the circadian phase to become independent of the subjective IC choice. The result showed that without an accurate IC, circadian phase estimation is dependent on subjective IC choice, meaning that circadian phase estimates in the first 17 days are not reliable. Indeed, these days were reduced to 13 - 15 days on average when previous studies IC estimation methods were used. To further shorten this length, we developed new IC estimation methods--period- and work history-based sleep methods--that incorporate daily variations in sleep history. Notably, the new methods reduced the number of days required for reliable circadian phase estimation to about seven days. Hence, our approach allows a larger portion of circadian phase estimates from given sleep data to be used as reliable information. The superiority of our methods paves the way for improved circadian phase estimation, ultimately enhancing the practicality of chronotherapy applications.

bioinformatics↗

bioRxiv2025

Toward Single-Cell Control: Noise-Robust Perfect Adaptation in Biomolecular Systems

Robust perfect adaptation (RPA), whereby a consistent output level is maintained even after a disturbance, is a highly desired feature in biological systems. This property can be achieved at the population average level by combining the well-known antithetic integral feedback (AIF) loop into the target network. However, the AIF controller amplifies the noise of the output level, disrupting the single-cell level regulation of the system output and compromising the conceptual goal of stable output level control. To address this, we introduce a new regulation motif, the noise controller, which is inspired by the AIF loop but differs by sensing the output levels through the dimerization of output species. Combining this noise controller with the AIF controller successfully maintained system output noise as well as mean at their original level, even after the perturbation, thereby achieving noise RPA. Furthermore, our noise controller could reduce the output noise to a desired target value, achieving a Fano factor as small as 1, the commonly recognized lower bound of intrinsic noise in biological systems. Notably, our controller remains effective as long as the combined system is ergodic, making it applicable to a broad range of networks. We demonstrate its utility by combining the noise controller with the DNA repair system of Escherichia coli, which reduced the proportion of cells failing to initiate the DNA damage response. These findings enhance the precision of existing biological controllers, marking a key step toward achieving single-cell level regulation.

systems biology↗

bioRxiv2025

A Fully Automated, Data-Driven Approach for Dimensionality Reduction and Clustering in Single-Cell RNA-seq Analysis

Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity but demands robust dimensionality reduction (DR) and clustering to handle high-dimensional, noisy data. Many DR and clustering approaches rely on user-defined parameters, undermining reliability. Even automated clustering methods like ChooseR and MultiK still employ fixed principal component defaults, limiting their full automation. To overcome this limitation, we propose a fully automated clustering approach by integrating scLENS--a method for optimal PC selection--with these tools. Our fully automated approach improves clustering performance by [~]14% for ChooseR and [~]10% for MultiK and identifies additional cell subtypes, highlighting the advantages of adaptive, data-driven DR. HighlightsO_LIFully automated, data-driven clustering pipeline for scRNA-seq analysis. C_LIO_LIAddresses the limitation of fixed principal component defaults in DR. C_LIO_LIData-driven pipeline improves clustering performance by 10-14%. C_LIO_LIPerformance gains are most pronounced on high-sparsity, high-skewness data. C_LI Author SummaryA fundamental goal in modern biology is to create detailed cellular maps of complex tissues to understand their function in health and disease. Achieving this level of detail is now possible through single-cell sequencing, a technology that generates massive, high-dimensional, and noisy datasets of individual cells genetic profiles. However, accurately identifying cell-types from these datasets remains a major analytical hurdle, as current computational methods rely on subjective parameters that compromise reliability and reproducibility. To address this challenge, we developed a fully automated pipeline that integrates scLENS, an adaptive noise filtering method, with automated cell grouping tools. By providing optimal parameters for noise filtering and cell grouping, our pipeline eliminates their reliance on subjective, fixed parameters, thereby significantly improving accuracy in cell-type classification. This tool, characterized by its high clustering efficiency, can accelerate discoveries in fields like cancer biology and immunology by providing a robust and reproducible analytical platform.

bioinformatics↗

bioRxiv2025

ETV4 restrains the formation of CD74+ cytotoxic exhausted CD8+ T cells

Exhausted CD8+ T (Tex) cells comprise heterogeneous subsets with varying cytotoxic and dysfunctional properties, making inhibitory receptor profiles unreliable indicators of function. Therefore, it is essential to define transcriptional regulators or markers that accurately reflect these states. Here, we identified ETS variant 4 (ETV4) as a transcription factor that restricts cytotoxic Tex differentiation within tumors. ETV4 deficiency promotes the emergence of a CD74+ cytotoxic Tex subset while preserving the progenitor Tex (Tpex) pool. CD74 serves as both a marker and regulator, reinforcing Tpex features and enhancing Tex cytotoxicity. Mechanistically, ETV4 loss derepresses T-bet expression, thereby increasing IFN{gamma} production that induces CD74 expression in a paracrine manner. Single-cell analyses across human cancers revealed that CD74+ Tex cells are enriched for proliferative and metabolic programs and correlate with improved survival. Collectively, we establish the ETV4-CD74 axis as a regulator of Tex fate and promising therapeutic target to augment anti-tumor immunity.

immunology↗

bioRxiv2025

daf-16/FOXO promotes the activity of ligand-bound DAF-12/NHR to coordinate dauer recovery and post-dauer seam cell fate

Animal development occurs in the context of varying environmental conditions. Many animal species can withstand adverse conditions by entering a stress-resistant and developmentally arrested diapause stage. If conditions improve, animals can recover and complete development. In Caenorhabditis elegans larvae that encounter adverse environments, dauer diapause can interrupt developmental progression after the second larval molt. During continuous (non-dauer) development, a heterochronic molecular timer comprised primarily of microRNAs and their targets controls the progression of stage-specific cell fates in lateral hypodermal seam cells. In unfavorable conditions, the DAF-16/FOXO transcription factor promotes dauer formation in part through opposing production of the ligand for the DAF-12 nuclear hormone receptor. Ligand-free DAF-12 promotes dauer formation and represses expression of heterochronic microRNAs in the let-7 family. Here, we show that daf-16 acts before dauer formation in anticipation of growth after dauer by promoting both dauer exit and developmental progression. We found that daf-16(0) post-dauer adults showed delayed dauer exit, reiterative heterochronic defects, and reduced expression of let-7-family microRNAs. These phenotypes were suppressed by addition of the DAF-12 ligand, dafachronic acid. Dafachronic acid is synthesized from cholesterol, and dauer larvae sequester cholesterol in the intestinal lumen until dauer exit. We found that a fluorescent cholesterol analog was not retained in daf-16 mutant larvae during dauer recovery. Timed auxin-mediated depletion of DAF-16 indicated that daf-16 is required before dauer formation to prevent reiterative seam cell fates in post-dauer animals. We propose a model whereby daf-16 acts prior to dauer formation to enable dafachronic acid synthesis by retaining cholesterol during dauer recovery. Ligand-bound DAF-12 then promotes dauer exit and expression of let-7-family microRNAs, thereby promoting developmental progression. Thus, daf-16 coordinates dauer entry with anticipation of recovery and post-dauer development.

developmental biology↗

bioRxiv2025

How Cells Tame Noise While Maintaining Ultrasensitive Transcriptional Responses

Ultrasensitive transcriptional switches are essential for converting gradual molecular inputs into decisive gene expression responses, enabling critical behaviors such as bistability and oscillations. While cooperative binding, relying on direct repressor-DNA binding, has been classically regarded as a key ultrasensitivity mechanism, recent theoretical works have demonstrated that combinations of indirect repression mechanisms--sequestration, blocking, and displacement--can also achieve ultrasensitive switches with greater robustness to transcriptional noise. However, these previous works have neglected key biological constraints such as DNA binding kinetics and the limited availability of transcriptional activators, raising the question of whether ultrasensitivity and noise robustness can be sustained under biologically realistic conditions. Here, we systematically assess the impact of these factors on ultrasensitivity and noise robustness under physiologically plausible conditions. We show that while various repression combinations can reduce noise, only the full combination of all three indirect mechanisms consistently maintains low noise and high ultrasensitivity. As a result, biological oscillators employing this triple repression architecture retain precise rhythmic switching even under high noise, and even when activators are shared across thousands of target genes. Our findings offer a mechanistic explanation for the frequent co-occurrence of these repression mechanisms in natural gene regulatory systems. AUTHOR SUMMARYCells must make accurate decisions in noisy environments using limited molecular resources. One essential tool for this is the ultrasensitive transcriptional switch, which enables sharp transitions in gene expression. While cooperative binding has long been viewed as the primary mechanism behind ultrasensitivity, it is highly sensitive to molecular noise. Our study explores how cells overcome this limitation by combining three indirect repression mechanisms: sequestration, blocking, and displacement. We show that this triple-repression architecture not only generates ultrasensitivity but also ensures noise robustness under physiologically realistic conditions--even when a single pool of transcription factors regulates thousands of genes. These findings reveal a biologically feasible strategy for noise-resilient transcription and offer a mechanistic explanation for why these repression strategies frequently co-occur in natural systems.

systems biology↗

bioRxiv2025

A Biosynthetic Thumb Prosthesis

In cases of severe damage to the extremities, the function and structure of compromised biological tissues must be replaced. If biological reconstruction using autologous tissue is not feasible, amputation and replacement with a synthetic prosthesis is often the next best option. Currently, the synthetic materials available for prostheses are limited, especially in their ability to restore skin sensation. Here we show a biosynthetic prosthesis that combines the versatility of titanium with the rich sensory capabilities of biological skin, to both increase movement and restore sensation to an amputated thumb. The prosthesis recreates opposition pinch by linking motion of the prosthetic joint to that of the residual biological joint. The device is enclosed in neurotized skin from the patients own body, providing natural sensation. We validated the biosynthetic thumbs ability to reproduce opposition pinch on the benchtop and in a cadaver, and showed in vivo viability of the skin interface in an animal model. These results provide a framework for functional reconstruction of amputated digits using a combination of synthetic materials and biological tissues. One Sentence SummaryA prosthetic thumb made up of a titanium mechanism and living skin can restore opposition pinch and touch sensation following thumb amputation.

bioengineering↗

bioRxiv2024

MORC-1 is a key component of the C. elegans CSR-1 germline gene licensing mechanism

The Argonaute CSR-1 is essential for germline development in C. elegans. Mutation of csr-1 downregulates thousands of germline-expressed genes, leading to the model that the CSR-1-mediated small RNA pathway promotes, or "licenses," gene expression by an unknown mechanism. CSR-1 also silences a limited number of genes through its canonical endonucleolytic "slicer" activity. We show that the GHKL-type ATPase MORC-1, a CSR-1 slicing target, over-accumulates at CSR-1 "licensed" target genes in csr-1(-), which correlates with ectopic gain of H3K9me3, H3K36me3 loss, and gene downregulation. Loss of morc-1 rescues csr-1(-) defects, while overexpressing MORC-1 in the germline of wild-type worms is sufficient to cause sterility and downregulate CSR-1 targets. These results show that MORC-1 overexpression in csr-1(-) is a primary driver of the CSR-1-mediated gene licensing mechanism. One-Sentence SummaryMORC-1 acts downstream of CSR-1 to regulate germline chromatin states and is a key component of the gene licensing mechanism.

molecular biology↗

bioRxiv2024

From Homogeneity to Heterogeneity: Refining Stochastic Simulations of Gene Regulation

Cellular processes are intricately controlled through gene regulation, which is significantly influenced by intrinsic noise due to the small number of molecules involved. The Gillespie algorithm, a widely used stochastic simulation method, is pervasively employed to model these systems. However, this algorithm typically assumes that DNA is homogeneously distributed through- out the nucleus, which is not realistic. In this study, we evaluated whether stochastic simulations based on the assumption of spatial homogeneity can accurately capture the dynamics of gene regulation. Our findings indicate that when transcription factors diffuse slowly, these simulations fail to accurately capture gene expression, highlighting the necessity to account for spatial heterogeneity. However, incorporating spatial heterogeneity considerably increases computational time. To address this, we explored various stochastic quasi-steady-state approximations (QSSAs) that simplify the model and reduce simulation time. While both the stochastic total quasi-steady state approximation (stQSSA) and the stochastic low-state quasi-steady-state approximation (slQSSA) reduced simulation time, only the slQSSA provided an accurate model reduction. Our study underscores the importance of utilizing appropriate methods for efficient and accurate stochastic simulations of gene regulatory dynamics, especially when incorporating spatial heterogeneity.

systems biology↗

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↗