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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

Pharmaceutical-grade Rigosertib is a Microtubule-destabilizing Agent

We recently used CRISPRi/a-based chemical-genetic screens and targeted cell biological, biochemical, and structural assays to determine that rigosertib, an anti-cancer agent in phase III clinical trials, kills cancer cells by destabilizing microtubules. In a recent manuscript, Reddy and co-workers suggest that this microtubule-destabilizing activity of rigosertib is mediated not by rigosertib itself but by a contaminating degradation product of rigosertib, ON01500, present in formulations obtained from commercial vendors (Baker et al., 2019). Here, we demonstrate that treatment of cells with pharmaceutical-grade rigosertib (>99.9% purity) results in qualitatively indistinguishable phenotypes as treatment with commercially obtained rigosertib across multiple assays. The two compounds have indistinguishable chemical-genetic interactions with genes involved in modulating the microtubule network (KIF2C and TACC3), both destabilize microtubules in cells and in vitro, and both show substantially reduced toxicity in cell lines expressing a rationally-designed mutant of tubulin (L240F TUBB mutant), in which the rigosertib binding site in tubulin is mutated. Importantly, the specificity of the L240F TUBB mutant for microtubule-destabilizing agents, which is disputed by Reddy and co-workers, was recently confirmed by an independent research group (Patterson et al., 2019). We conclude that rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings.

cancer biology↗

bioRxiv2018

On the Biological Signalling, Information and Estimation Limits of Birth Processes

Understanding and uncovering the mechanisms or motifs that molecular networks employ to regulate noise is a key problem in cell biology. As it is often difficult to obtain direct and detailed insight into these mechanisms, many studies instead focus on assessing the best precision attainable on the signalling pathways that compose these networks. Molecules signal one another over such pathways to solve noise regulating estimation and control problems. Quantifying the maximum precision of these solutions delimits what is achievable and allows hypotheses about underlying motifs to be tested without requiring detailed biological knowledge. The pathway capacity, which defines the maximum rate of transmitting information along it, is a widely used proxy for precision. Here it is shown, for estimation problems involving elementary yet biologically relevant birth-process networks, that capacity can be surprisingly misleading. A time-optimal signalling motif, called birth-following, is derived and proven to better the precision expected from the capacity, provided the maximum signalling rate constraint is large and the mean one above a certain threshold. When the maximum constraint is relaxed, perfect estimation is predicted by the capacity. However, the true achievable precision is found highly variable and sensitive to the mean constraint. Since the same capacity can map to different combinations of rate constraints, it can only equivocally measure precision. Deciphering the rate constraints on a signalling pathway may therefore be more important than computing its capacity.

systems biology↗

bioRxiv2020

In vivo fluorescence lifetime imaging captures metabolic changes in macrophages during wound responses in zebrafish

The effector functions of macrophages across the spectrum of activation states in vitro are linked to profound metabolic rewiring. However, the metabolism of macrophages remains poorly characterized in vivo. To assess changes in the intracellular metabolism of macrophages in their native inflammatory microenvironment, we employed two-photon fluorescence lifetime imaging microscopy (FLIM) of metabolic coenzymes NAD(P)H and FAD. We found that pro-inflammatory activation of macrophages in vivo was associated with a decrease in the optical redox ratio [NAD(P)H/(NAD(P)H+FAD)] relative to a pro-resolving population during both infected and sterile inflammation. FLIM also resolved temporal changes in the optical redox ratio and lifetime variables of NAD(P)H in macrophages over the course of sterile inflammation. Collectively, we show that non-invasive and label-free imaging of autofluorescent metabolic coenzymes is sensitive to dynamic changes in macrophage activation in interstitial tissues. This imaging-based approach has broad applications in immunometabolism by probing in real time the temporal and spatial metabolic regulation of immune cell function in a live organism. SignificanceMetabolic regulation of macrophage effector functions has recently emerged as a key concept in immune cell biology. Studies rely on in vitro and ex vivo approaches to study macrophage metabolism, however the high plasticity of these cells suggest that removal from their native microenvironment may induce changes in their intracellular metabolism. Here, we show that fluorescence lifetime imaging microscopy of metabolic coenzymes captures dynamic changes in the metabolic activity of macrophages while maintaining them in their endogenous microenvironment. This approach also resolves variations on a single-cell level, in contrast to bulk measurements provided by traditional biochemical assays, making it a potentially valuable tool in the field of immunometabolism.

immunology↗

bioRxiv2023

Active mesh and neural network pipeline for cell aggregate segmentation

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology, due to improvements in capacity and accuracy of microscopy techniques. Here we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary duct organoids imaged over 24 hours with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin which implement active meshes deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction. Statement of significanceIn vitro culture of organ-like structures derived from stem cells, so-called organoids, allows to image tissue morphogenetic processes with high temporal and spatial resolution. Three-dimensional segmentation of cell shape in timelapse movies of these developing organoids is however a significant challenge. In this work, we propose an image analysis pipeline for cell aggregates that combines deep learning with active contour segmentations. This combination offers a flexible and efficient way to segment three-dimensional cell images, which we illustrate with by segmenting datasets of growing mammary gland organoids and mouse embryonic stem cells.

bioinformatics↗

bioRxiv2017

Single-cell RNA-sequencing redefines blood cell type classification in mosquitoes

Mosquito blood cells are ancestral immune cells that help control infection by vector-borne pathogens. Despite their importance, little is known about mosquito blood cell biology beyond the ambiguous morphological and functional criteria used for their classification. Here we combined the power of single-cell RNA-sequencing, imaging flow cytometry and single-molecule RNA hybridization to analyze blood cells of the malaria mosquito Anopheles gambiae. By demonstrating that blood cells express nearly half of the mosquito transcriptome, our dataset represents an unprecedented view into their transcriptional machinery. Analyses of differentially expressed genes identified transcriptional signatures of two distinct cell types that challenge the current morphology-based classification of these cells. We further demonstrated an active transfer of a cellular marker between blood cells that confounds their identity. We propose that cell-to-cell exchange is broadly relevant for cell type classification and may account for the remarkable cellular diversity observed in nature.

immunology↗

bioRxiv2019

Transcriptomically-guided mesendoderm induction of human pluripotent stem cells using a systematically defined culture scheme

Human pluripotent stem cells (hPSCs) are an essential cell source in tissue engineering, studies of development, and disease modeling. Efficient, broadly amenable protocols for rapid lineage induction of hPSCs are of great interest in the stem cell biology field. We describe a simple, robust method for differentiation of hPSCs into mesendoderm in defined conditions utilizing single-cell seeding (SCS) and BMP4 and Activin A (BA) treatment. Gene sets and gene ontology terms related to mesoderm and endoderm differentiation were enriched after 48 hours of BA treatment. BA treatment was readily incorporated into existing protocols for chondrogenic and endothelial progenitor cell differentiation. After prolonged differentiation in vitro or in vivo, BA pre-treatment resulted in higher mesoderm and endoderm levels at the expense of ectoderm formation. These data demonstrate that SCS with BA treatment is a powerful method for induction of mesendoderm that can be integrated into protocols for mesoderm and endoderm differentiation.

bioengineering↗

bioRxiv2016

Inferring time-derivatives, including cell growth rates, using Gaussian processes

Often the time-derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time-derivatives as a function of time from time-series data. Our approach is based on established properties of Gaussian processes and therefore applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, allows interpolation with the corresponding error estimation, and can be applied to any number of experimental replicates. As illustrations, we infer growth rate from measurements of the optical density of populations of microbial cells and estimate the rate of in vitro assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies in a single yeast cell. Being accessible through both a GUI and from scripts, our algorithm should have broad application across the sciences.

Microbiology↗

bioRxiv2024

A model for enhancer selection in dense regulatory networks captures the dynamics of cell type specification

Understanding how cell identity is encoded in the genome and acquired during differentiation is a central challenge in cell biology. We derive a theoretical framework called "EnhancerNet" that models dense feedback networks involving transcription factors and enhancers, which can be parameterized from terminal cell identities without fitting unobserved variables. EnhancerNet recapitulates the dynamics of enhancer selection and cell type specification via two distinct pathways: direct reprogramming or differentiation through transient, multipotent progenitor states. These pathways capture the hallmarks of their respective counterparts in animal cells, with the model reproducing known reprogramming recipes and the complex hematopoietic differentiation hierarchy. Using EnhancerNet, we show that hierarchical progenitors emerge as transient states during differentiation and propose a method to predict their identity from terminal states. The model explains how new cell types could evolve and highlights the functional importance of distal regulatory elements with dynamic chromatin in multicellular evolution.

systems biology↗

bioRxiv2019

A developmental gene regulatory network for invasive differentiation of the C. elegans anchor cell

Cellular invasion is a key part of development, immunity, and disease. Using the in vivo model of C. elegans anchor cell invasion, we characterize the gene regulatory network that promotes invasive differentiation. The anchor cell is initially specified in a stochastic cell fate decision mediated by Notch signaling. Previous research has identified four conserved transcription factors, fos-1a (Fos), egl-43 (EVI1/MEL), hlh-2 (E/Daughterless) and nhr-67 (NR2E1/TLX), that mediate anchor cell specification and/or invasive differentiation. Connections between these transcription factors and the underlying cell biology that they regulate is poorly understood. Here, using genome editing and RNA interference, we examine transcription factor interactions prior to and after anchor cell specification. During invasion we identify that egl-43, hlh-2, and nhr-67 function together in a type I coherent feed-forward loop with positive feedback. Conversely, prior to specification, these transcription factors function independent of one another to regulate LIN-12 (Notch) activity. Together, these results demonstrate that, although the same transcription factors can function in fate specification and differentiated cell behavior, a gene regulatory network can be rapidly re-wired to reinforce a post-mitotic, pro-invasive state.\n\nSUMMARY STATEMENTBasement membrane invasion by the C. elegans anchor cell is coordinated by a dynamic gene regulatory network encompassing cell cycle dependent and independent sub-circuits.

developmental biology↗

bioRxiv2024

Chromatin remodeling protein BPTF regulates transcriptional stability in planarian stem cells

Trimethylation of histone H3 lysine 4 (H3K4me3) correlates strongly with gene expression in many different organisms, yet the question of whether it plays a causal role in transcriptional activity remains unresolved. Although H3K4me3 does not directly affect chromatin accessibility, it can indirectly affect genome accessibility by recruiting the ATP-dependent chromatin remodeling complex NuRF (Nucleosome Remodeling Factor). The largest subunit of NuRF, BPTF/NURF301, binds H3K4me3 specifically and recruits the NuRF complex to loci marked by this modification. Studies have shown that the strength and duration of BPTF binding likely also depends on additional chromatin features at these loci, such as lysine acetylation and variant histone proteins. However, the exact details of this recruitment mechanism vary between studies and have largely been tested in vitro. Here, we use stem cells isolated directly from live planarian animals to investigate the role of BPTF in regulating chromatin accessibility in vivo. We find that BPTF operates at gene promoters and is most effective at facilitating transcription at genes marked by Set1-dependent H3K4me3 peaks, which are significantly broader than those added by the lysine methyltransferase MLL1/2. Moreover, BPTF is essential for planarian stem cell biology and its loss of function phenotype mimics that of Set1 knockdown. Together, these data suggest that BPTF and H3K4me3 are important mediators of both transcription and in vivo stem cell function.

developmental biology↗

bioRxiv2021

TSC22D4 interacts with Akt1 in response metabolic and stress signals

Transforming Growth Factor {beta} 1 Stimulated Clone 22 D4 (TSC22D4) is an intrinsically disordered protein that regulates cellular and physiological processes such as cell proliferation, cellular senescence as well as hepatic glucose and lipid metabolism. The molecular mechanism of TSC22D4 action in these cellular and metabolic functions, however, remains largely elusive. Here, we identified TSC22D4 as a novel protein kinase B/Akt1 interacting protein, a critical mediator of insulin/PI3K signaling pathway implicated in diverse set of diseases including type 2 diabetes, obesity and cancer. TSC22D4 interacts with Akt1 not constitutively but rather in a regulatory manner. While glucose and insulin stimulation of cells or refeeding of mice impair the hepatic TSC22D4-Akt1 interaction, inhibition of mitochondria and oxidative stress, promote it; indicating that extra- and intra-cellular cues play a key role in controlling TSC22D4-Akt1 interaction. Our results also demonstrate that together with its dimerization domain, i.e. the TSC box, TSC22D4 requires its intrinsically disordered region (D2 domain) to interact with Akt1. To understand regulation of TSC22D4 function further, we employed tandem mass spectrometry and identified 15 novel phosphorylation sites on TSC22D4. Similar to TSC22D4-Akt1 interaction, TSC22D4 phosphorylation also responds to environmental signals such as starvation, mitochondrial inhibition and oxidative stress. Interestingly, 6 out of the 15 novel phosphorylation sites lie within the TSC22D4 D2 domain, which is required for TSC22D4-Akt1 interaction. Characterization of the regulation and function of these novel phosphorylation sites, in the future, will shed light on our understanding of the role of TSC22D4-Akt1 interaction in both cell biological and physiological functions. Overall, our findings postulate a model whereby TSC22D4 acts as an environmental sensor and interacts with Akt1 to regulate cell proliferation, cellular senescence as well as maintain metabolic homeostasis.

molecular biology↗

bioRxiv2026

SOX11 stimulates γδ T-cell differentiation and synergizes with LMO2 and MYCN to drive γδ-like T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is a heterogeneous hematologic malignancy in which LMO2 {gamma}{delta}-like T-ALL represents a rare but clinically aggressive subtype associated with poor treatment response and inferior survival. Integrated transcriptomic analyses identified high SOX11 expression as a defining feature of high-risk LMO2 {gamma}{delta}-like T-ALL, where elevated SOX11 levels correlated with refractory disease and poor clinical outcome. To investigate the functional role of SOX11 in {gamma}{delta} T-cell biology and leukemogenesis, we generated a conditional R26-SOX11 mouse model enabling lineage-specific SOX11 overexpression in T-cell progenitors. SOX11 expression promoted expansion of the innate {gamma}{delta} T-cell compartment in thymus, spleen, and bone marrow, accompanied by transcriptional activation of {gamma}{delta} T-cell differentiation, activation, and cytotoxicity programs. However, SOX11 overexpression alone was insufficient to induce leukemia or confer thymocyte self-renewal capacity. In contrast, combined SOX11 and LMO2 overexpression markedly accelerated T-ALL development and strongly increased the incidence of {gamma}{delta}-like leukemias, thereby recapitulating the human high-risk LMO2 {gamma}{delta}-like T-ALL subtype. Mechanistically, SOX11 expanded the pre-leukemic DN3 thymocyte compartment in LMO2-driven mouse model while promoting differentiation toward the {gamma}{delta} lineage. Transcriptomic profiling identified activation of MYCN-associated transcriptional programs in SOX11/LMO2 pre-leukemic thymocytes. Consistently, MYCN was highly expressed in human LMO2 {gamma}{delta}-like T-ALL, and recurrent stabilizing MYCN P44L mutations were enriched in this subtype. Functional validation using genetic and transplantation-based mouse models demonstrated that SOX11 cooperates with MYCN to accelerate T-ALL onset. Together, these findings establish a cooperative SOX11-MYCN oncogenic axis driving {gamma}{delta}-like T-ALL and provide a novel preclinical model for investigating therapeutic vulnerabilities in this high-risk leukemia subtype.

cancer biology↗

bioRxiv2020

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm

Stochasticity from gene expression in single cells is known to drive metabolic heterogeneity at the level of cellular populations, which is understood to have important consequences for issues such as microbial drug tolerance and treatment of human diseases like cancer. Despite considerable advancements in profiling the genomes, transcriptomes, and proteomes of single cells, it remains difficult to experimentally characterise their metabolism at genome-scale. Computational methods could bridge this gap toward a systems understanding of single-cell biology. To address this challenge, we developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), a computational framework for simulating the stochastic dynamics of the metabolism of individual cells using genome-scale metabolic models with experimental estimates of gene expression and enzymatic reaction rate parameters. SSA-FBA extends the constraint-based modelling formalism of metabolic network modelling to the single-cell regime, enabling simulation when experimentation is intractable. We also developed an efficient implementation of SSA-FBA that leverages the topology of embedded FBA models to significantly reduce the computational cost of simulation. As a preliminary case study, we built a reduced single-cell model of Mycoplasma pneumoniae, and used SSA-FBA to illustrate the role of stochasticity on the dynamics of metabolism at the single-cell level. SIGNIFICANCEDue to fundamental challenges limiting the experimental characterisation of metabolism within individual cells, computational methods are needed to help infer the metabolic behaviour of single cells from information about their transcriptomes and proteomes. In this paper, we present SSA-FBA, the first systematic framework for modelling the stochastic dynamics of single cells at the level of genome-scale metabolic reaction networks. We provide a robust and efficient algorithm for simulating SSA-FBA models, and apply it to a case study involving the metabolism, RNA and protein synthesis and turnover of a single Mycoplasma pneumoniae cell.

systems biology↗

bioRxiv2021

Fission stories: Using PomBase to understand Schizosaccharomyces pombe biology

PomBase (www.pombase.org), the model organism database (MOD) for the fission yeast Schizosaccharomyces pombe, supports research within and beyond the S. pombe community by integrating and presenting genetic, molecular, and cell biological knowledge into intuitive displays and comprehensive data collections. With new content, novel query capabilities, and biologist-friendly data summaries and visualisation, PomBase also drives innovation in the MOD community.

bioinformatics↗

bioRxiv2022

Chromatin phase separated nanoregions regulated by cross-linkers and explored by single particle trajectories

Phase separated domains (PSDs) are ubiquitous in cell biology, representing nanoregions of high molecular concentration. PSDs appear at diverse cellular domains, such as neuronal synapses but also in eukaryotic cell nucleus, limiting the access of transcription factors and thus preventing gene expression. We study here the properties of PSDs and in particular how they can be generated by polymers. We show that increasing the number of cross-linkers generate a polymer condensation, preventing the access of diffusing molecules. To investigate how the PSDs restrict the motion of diffusing molecules, we estimate the mean residence and first escaping times. Finally, by computing the mean square displacement of single particle trajectories, we can reconstruct the properties of PSDs in term of a continuum range of anomalous exponents. To conclude, PSDs can result from a condensed chromatin, where the number of cross-linkers control the molecular access.

genetics↗

bioRxiv2023

LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data

Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. Moreover, Lin Race can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.

bioinformatics↗

bioRxiv2024

Smooth muscle expression of RNA editing enzyme ADAR1 controls vascular integrity and progression of atherosclerosis

Mapping the genomic architecture of complex disease has been predicated on the understanding that genetic variants influence disease risk through modifying gene expression. However, recent discoveries have revealed that a significant burden of disease heritability in common autoinflammatory disorders and coronary artery disease (CAD) is mediated through genetic variation modifying post-transcriptional modification of RNA through adenosine-to-inosine (A-to-I) RNA editing. This common RNA modification is catalyzed by ADAR enzymes, where ADAR1 edits specific immunogenic double stranded RNA (dsRNA) to prevent activation of the double strand RNA (dsRNA) sensor MDA5 (IFIH1) and stimulation of an interferon stimulated gene (ISG) response. Multiple lines of human genetic data indicate impaired RNA editing and increased dsRNA sensing by MDA5 to be an important mechanism of CAD risk. Here, we provide a crucial link between observations in human genetics and mechanistic cell biology leading to progression of CAD. Through analysis of human atherosclerotic plaque and culture of human coronary artery vascular smooth muscle cells (SMCs) we implicate the SMC to have a distinct requirement for RNA editing, and that MDA5 activation regulates SMC phenotypic modulation. Through generation of a conditional SMC specific Adar1 deletion mouse model on a pro-atherosclerosis background with additional constitutive deletion of MDA5 (Ifih1), and with incorporation of single cell RNA sequencing cellular profiling, we further show that Adar1 controls SMC phenotypic state by regulating Mda5 activation, is required to maintain vascular integrity, and controls progression of atherosclerosis and vascular calcification. Finally, we further corroborate our findings in a large human carotid endarterectomy dataset (Athero-Express) where we show that ISG activation is strongly associated with decreased plaque stability, increased SMC phenotypic modulation, and increased plaque calcification. Through this work, we describe a fundamental mechanism of CAD, where cell type and context specific RNA editing and sensing of dsRNA mediates disease progression, bridging our understanding of human genetics and disease causality. One Sentence SummarySmooth muscle expression of RNA editing enzyme ADAR1 regulates activation of double strand RNA sensor MDA5 in novel mechanism of atherosclerosis.

genomics↗

bioRxiv2025

Dynamic translocation of Inside-Out proteins to the cell surface underlies cellular adaptation to cancer-induced stress

Inside-out (I-O) protein display, the non-canonical surface localization of intracellular proteins, represents an underexplored feature of tumor cell biology. Here, we map the molecular landscape and trafficking mechanisms that control the presentation of I-O proteins on cancer cell membranes. Employing APEX2-mediated proximity biotinylation and a custom antibody generation and validation platform, we identified approximately 140 high-confidence I-O proteins, primarily ribosomal, proteasomal, chaperone, and translation factors, notably enriched in protein families associated with stress-response pathways. Validation of 500 antibodies encompassing 40 I-O targets across seven tumor cell lines confirmed selective and robust surface localization, while in vivo imaging in mouse xenografts demonstrated pronounced and tumor-specific antibody accumulation. I-O proteins were absent on PBMCs and in normal tissues, indicating cancer cell selectivity. Functional analyses revealed that I-O protein tethering to the membrane is dependent on heparan sulfate interactions; enzymatic removal of these glycans led to the clearance of I-O proteins from the cell surface. Notably, the removed proteins returned to baseline levels within six hours, indicating a dynamic balance related to ER-Golgi trafficking and cellular stress. Nearly half of these I-O proteins overlapped with known stress granule components; however, stress elements that promote stress granule formation do not similarly affect surface display of I-O proteins. Furthermore, I-O proteins are present on standard cancer cell lines under lower stress levels needed to induce stress granule formation, suggesting parallel yet mechanistically distinct aspects of the stress response. These findings position I-O display as a new paradigm in protein trafficking, different from traditional secretion pathways and closely linked to stress response.

biochemistry↗