Biology subjects

Wu, A.

Publications and source records attributed to Wu, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Xylulose 5-Phosphate/Phosphate Translocator Mediates Carbon Allocation for Anabolic Metabolism in the Chlamydomonas Chloroplast

Metabolite movement into chloroplasts is essential for sustaining chloroplast anabolic metabolism and cellular growth, and yet how the specific factors/transporters that enable import and support metabolism under heterotrophic conditions (in the dark in the presence of fixed carbon) remain poorly understood. Here, we identify CreTPT10, a chloroplast envelope-localized transporter in the unicellular green alga Chlamydomonas reinhardtii (Chlamydomonas throughout) that, of the substrates tested, has the highest specificity for xylulose 5-phosphate (X5P). We also demonstrated that the loss of CreTPT10 caused a pronounced reduction in growth and respiratory activity in the dark, whereas no growth defects were observed in the tpt10 mutants when they were maintained in the light or under nutrient-limiting conditions. Furthermore, dark-grown tpt10 mutants exhibited markedly reduced levels of lipids, nucleotides, isoprenoids, and aromatic secondary metabolites, accompanied by coordinated repression of genes encoding enzymes associated with chloroplast-localized biosynthetic pathways. This metabolic suppression extended beyond the chloroplast, as genes associated with the mitochondrial respiratory chain and cell cycle progression were markedly downregulated in darkness. Together, these findings indicate that X5P import via CreTPT10 is critical for sustaining chloroplast anabolic metabolism and functionally coordinates chloroplast and mitochondrial energy metabolism to support heterotrophic growth.

plant biology↗

bioRxiv2026

Compression Efficiency and Structural Learning as a Computational Model of DLN Cognitive Stages

We propose a computational instantiation of three cognitive stages from the Dot-Linear- Network (DLN) framework, grounded in a compression-efficiency thesis. DLN stages are characterized as graph-structured belief-dependency representations used to evaluate options: Dot as no persistent belief graph (reactive policies with negligible internal state), Linear as a null graph over option beliefs (K independent option estimates with no information sharing), and Network as shared latent structure (a bipartite factor graph in which F latent factors connect to K options), augmented by a temporal exposure state and an explicit structural learning cycle (hypothesis [->] test [->] update/expand). We distinguish two compression targets--option-factor structure (shared components in expected outcomes) and stakes-factor structure (shared drivers of consequence-bearing exposures)-- whose intersection yields jointly efficient actions that simultaneously improve expected outcomes and marginal exposure impact. In a bandit-like simulation (100 seeds, K [isin] { 20, 50, 100, 200 }, F =5), Network policies dominate Linear policies in cost-adjusted utility at large K, with the empirical crossover occurring much earlier than an analytic cost-only prediction (K* = F + cmeta/cparam), revealing that the advantage is primarily statistical (shrinkage-like estimation gains from factor pooling) rather than purely computational. Under stakes, all non-DLN agents--including Linear-Plus agents with identical factor structure and Network-standard agents with hierarchical Bayesian learning--collapse due to unmodeled cumulative exposure, while Network-DLN maintains positive utility. Within-stage consistency tests (two algorithmically distinct agents per stage) confirm that the collapse pattern is determined by representational topology, not algorithmic choice. These results evaluate internal consistency of a DLN-to-computation mapping under explicit assumptions; they do not validate a developmental theory in humans.

neuroscience↗

bioRxiv2026

Environmental Amino Acid Sensing Regulates the Rate of ASC Translation and NLRP3 Inflammasome Assembly

The NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome is a multiprotein signaling complex that triggers pyroptotic cell death and interleukin (IL)-1 family cytokine release during infection and cell injury. Its assembly is driven by the adaptor protein, apoptosis-associated speck-like protein containing a CARD (ASC), whose filamentation forms a supramolecular speck upon NLRP3 activation to amplify inflammasome signaling. While the NLRP3 inflammasome is well appreciated as a sensor of environmental danger and damage, little is known about how homeostatic environmental factors like dietary metabolites regulate its activity. Here, we find that environmental availability of the branched-chain amino acids (BCAAs), leucine, isoleucine, and valine, controls NLRP3 inflammasome assembly. While ASC is typically viewed as a constitutively expressed, unregulated inflammasome component, we find that Toll-like receptor 4 (TLR4) activation triggers localization of ASC mRNA to the perinuclear space. Moreover, our data demonstrate that ASC undergoes TLR4-driven translational bursting from polyribosomes during inflammasome priming. This translational engagement is dependent on BCAA availability and mechanistic target of rapamycin (mTOR) activity, which regulate the kinetics of inflammasome assembly. In contrast, the translation of NLRP3 and caspase-1 is largely insensitive to these inputs. Furthermore, we find that BCAAs regulate NLRP3 inflammasome activation in both mouse and human macrophages, in the context of bacterial infection, and during lipopolysaccharide (LPS)-induced sepsis in vivo. Altogether, this work unveils a novel inflammasome priming event governed by the amino acid environment. These findings further highlight how the activity of proteins maintained in equilibrium like ASC can be dynamically regulated through rapid changes in mRNA translation.

immunology↗

bioRxiv2026

Glucose availability tunes latent CD8+ T cell expansion potential through a mitogen-independent, mTOR-dependent regulatory switch

Mitogenic signals are understood to license cell cycle progression and the metabolic reprogramming required for cell division, with acquired nutrients serving as permissive substrates. Here, we show that nutrient availability instead functions as a mitogen-independent regulatory input that dynamically controls CD8+ T cell proliferative potential. Activating stimuli have been shown to set T cell expansion capacity through their control of c-Myc expression, with the rate of c-Myc decay functioning as a division timer. We demonstrate that nutrient availability is sufficient to control c-Myc expression dynamics and dictates how division potential is stored and later actualized. Glucose-restricted T cells sustain proliferative potential and exhibit high AKT and ERK phosphorylation, despite limited growth. Upon glucose restoration, these cells rapidly increase c-Myc expression, accelerate through the cell cycle, and return to the expansion potential of glucose-replete controls, even after days of enforced restriction. Glucose restriction thus maintains a latent metabolic and mitogenic signaling state that is rapidly realized upon recovery. Mechanistically, mTOR signaling is required for this glucose recovery-driven proliferation, despite c-Myc and pERK remaining elevated following mTOR inhibition, indicating that glucose and mitogen signals operate through parallel rather than hierarchical control points. Altogether, these findings reveal that nutrient availability is not merely rate-limiting for proliferation but dictates the kinetics at which mitogenic signals are dissipated and realized. While mitogenic and nutrient cues converge on a shared anabolic network, they operate through distinct regulatory arms to coordinate the tempo and magnitude of clonal expansion, with implications for protective immunity and immunotherapy. Significance StatementCD8+ T cells rapidly proliferate to fight infections and cancer, often in variable nutrient environments. Activation signals are understood to control T cell expansion potential by setting c-Myc expression and its subsequent decay, with nutrients providing fuel. Here we find that glucose availability functions as an independent regulatory switch. Glucose-restricted T cells remain proliferatively poised for days, keeping pro-growth signaling and metabolic capacity primed. Upon glucose restoration, cells undergo a proliferative burst and catch up to glucose-replete counterparts. Although c-Myc expression rises upon glucose restoration, accelerated division kinetics instead require mTOR activity. These findings reveal that nutrient availability operates in parallel with mitogenic signaling, tuning the rate at which T cells store and realize their expansion potential.

immunology↗

bioRxiv2026

Distinct sensing of BCAAs by mTOR and c-Myc governs T cell proliferation, independent of catabolism

The branched chain amino acids (BCAAs: leucine, isoleucine, valine) are essential amino acids that function as catabolic substrates and signaling molecules via mTORC1. While individual BCAAs have unique roles in organismal and cellular physiology, the mechanisms underlying their individual effects remain poorly understood. We demonstrate that the three BCAAs have distinct roles in T cell biology. We find that isoleucine and valine are necessary and sufficient for quiescence exit and cell division, whereas leucine is dispensable. Mechanistically, these effects are independent of their diverging catabolic fates and instead due to differential sensing of leucine and isoleucine/valine by mTOR and c-Myc. While isoleucine and valine are necessary and sufficient for c-Myc expression, mTORC1 leucine-sensing represses c-Myc and proliferation during BCAA restriction. Together, we find that the discrete sensing of the BCAAs uncouples two major anabolic regulators, mTORC1 and c-Myc, in cell growth. This provides mechanistic insight into the distinct roles of the BCAAs in cell physiology, highlighting divergent BCAA sensing rather than catabolism, and offering a new lens to appreciate their impact on immunity and pathophysiology.

immunology↗

bioRxiv2025

Multi-stem-loop constrained library enables accelerated SELEX for aptamers with superior kinetics and affinity

AbstractAptamers are single-stranded nucleic acids with ligand-binding capacity as cost-effective alternatives to antibodies. However, their utility is often undermined by the inherent conformational instability of single-stranded nucleic acids, which can compromise binding kinetics and final affinity. To overcome this critical limitation, we synthesized a novel primary library incorporating multiple inlaid stem-loop structures to enhance molecular stability and rigidity. This structural constraint dramatically accelerated the selection process, allowing us to isolate aptamer candidates with superior affinity in just three rounds of SELEX (Systematic Evolution of Ligands by EXponential enrichment), a significant reduction compared to conventional methodologies. Critically, the resulting aptamer demonstrated higher affinity and faster binding kinetics compared to previously reported sequences, which directly validate our hypothesis that structural stabilization leads to high-performance aptamers. This methodology, which integrated rational conformational constraint with a high-throughput selection process, offered a generalizable strategy to efficiently select aptamers with excellent kinetic and thermodynamic performance for demanding applications such as continuous, real-time ligand detection.

biochemistry↗

bioRxiv2025

Viral protease-Initiated Pyroptosis Activator mRNA therapy as a Universal Antiviral Strategy

Although therapeutic drugs targeting gasdermin (GSDM)-mediated pyroptosis have made remarkable progress in treating various diseases, their potential for antiviral therapy remains largely unexplored. Inspired by the autoinhibitory mechanism of GSDM proteins and the clinical success of mRNA vaccines, herein we propose a concept of viral protease-initiated pyroptosis (VIP) mRNA therapy as a universal antiviral strategy. Using Hepatitis A virus (HAV) as a model, we rationally engineered an HAV-specific VIP activator (VIPA) by replacing the native gasdermin D (GSDMD) cleavage motif with that of HAV 3C protease. Expression of this VIPA selectively triggered pyroptosis in HAV-infected cells, thereby terminating viral replication. Remarkably, in vivo delivery of VIPA mRNA via lipid nanoparticles (LNPs) significantly suppressed viral replication and shedding, alleviated liver injury, and elicited a pyroptosis-driven antiviral immune response in uninfected cells. Similarly, VIPA against Zika virus (ZIKV) also exhibited potent antiviral efficacy in both cell culture and mouse models. Furthermore, a generative artificial intelligence (AI) platform was deployed to design de novo cleavage motifs for the SARS-CoV-2 main protease NSP5, together with experimental screening, yielding optimized VIPAs with enhanced antiviral potency. Our work establishes the concept of VIPA mRNA therapy as a versatile platform for the treatment of diverse viral infections via "kill-and-alert" mechanism.

microbiology↗

bioRxiv2025

Deciphering the Antigenic Evolution of Seasonal Influenza A Viruses with PREDAC-Transformer: From Antigenic Clustering to Key Site Identification

Seasonal influenza viruses undergo continuous antigenic drift due to mutations in the hemagglutinin (HA) protein, rendering vaccines ineffective and posing a significant global public health challenge. Existing computational models can predict antigenic relationships but generally lack interpretability, making it difficult to reveal the molecular determinants driving antigenic changes. To address this, we developed PREDAC-Transformer, an end-to-end deep learning framework that integrates sequence, physicochemical, and evolutionary features, and employs a self-attention mechanism to capture long-range dependencies relevant to antigenicity. More importantly, we introduce the integrated antigenicity score, which combines attention-based attribution with information-theoretic metrics to provide continuous quantification and ranking of the antigenic contributions of individual amino acid site. Our results demonstrate that PREDAC-Transformer not only significantly improves the accuracy of antigenic relationship prediction but also successfully recapitulates major historical antigenic cluster transitions. Using integrated antigenicity score, we systematically identified two classes of key sites: global key sites with sustained impact on antigenic evolution, and cluster-transition determining sites that drive cluster transitions. These sites include most canonical epitopes and reveal additional functional residues previously overlooked, which may influence immune escape via cooperative effects or glycosylation. Collectively, these findings advance our understanding of influenza antigenic evolution and provide novel insights for refining computational models. PREDAC-Transformer achieves high-precision prediction while attributing antigenic differences to individual residues, thereby linking viral genomic variation, antigenic change, and public health decision-making. This framework has the potential to reduce experimental burdens in influenza surveillance and assist in vaccine strain recommendation, thereby supporting global influenza control efforts.

bioinformatics↗

bioRxiv2025

Clinical Interventions and Inflammatory Signaling Shape the Transcriptional and Cellular Architecture of the Early Postnatal Lung

The early postnatal period in human development is characterized by extensive remodeling of the distal lung to support gas exchange, but this critical period remains poorly understood. Here, we constructed a comprehensive cellular atlas of the early postnatal human lung (0 to 2 years) using single-nucleus RNA sequencing of histologically normal specimens from 23 individuals. Our analysis identified two previously unknown and mutually exclusive transcriptional states of alveolar type 2 (AT2) cells, one defined by upregulation of genes involved in lipid metabolism and identified by unique expression of FMO5, and the other defined by upregulation of inflammatory response genes and identified by unique expression of CFTR. Using spatial transcriptomics, we discovered that AT2 cell states reside in specific niches and interact with distinct alveolar fibroblast subtypes. Clinical data and organoid experiments further suggest that the environment dictates which state prevails as the pro-inflammatory/pro-regenerative signals TNF- and IL-1{beta} promoted the CFTR+ state in vitro, while patients treated with the anti-inflammatory drug dexamethasone (Dex) had more abundant FMO5+ AT2 cells, and Dex induced the FMO5+ state in vitro. These observations link inflammatory signaling and anti-inflammatory clinical interventions to shifts in transcriptional state of the alveolar epithelium. We benchmarked two neonatal lung diseases, bronchopulmonary dysplasia and pulmonary interstitial glycogenosis, revealing a profound disruption in the balance of AT2 states, a broad arrest of postnatal cellular development, and impaired cellular maturation. Our work uncovers a fundamental new understanding of early postnatal human lung biology, linking pro- and anti-inflammatory signaling to AT2 transcriptional phenotypes and providing a new framework for understanding lung disease.

developmental biology↗

bioRxiv2025

A scalable long-term in vitro model of human colonic epithelium with continuous barrier function

Background & AimsHuman in vitro models of differentiated intestinal epithelium are valuable research tools for discovery and translational medicine. Primary adult colonic epithelium barrier function is difficult to assess in a 2-dimensional (2D) in vitro format for more than a few days due to rapid cellular turnover. We developed a transwell model of the colonic epithelium that maintains proliferative and differentiated cell types, allowing long-term culture. MethodsUsing 3-dimensional (3D) primary human colonoids, we seeded dissociated epithelium onto 96-well transwell membranes and added different media that was growth factor specific to apical or basolateral transwell chambers. Barrier integrity was assessed using a variety of stimuli and TEER was continuously monitored in real time for up to 2 weeks, followed by quantitative single-cell image analysis using high-content imaging and deep-learning-based cell segmentation. ResultsEpithelium cultured up to 30 days sustained a time-dependent differentiation, leading to the establishment of a monolayer with proliferative stem/progenitor and differentiated cell types. While monitoring continuous TEER across multiple donor organoids, barrier challenge assays recorded donor-specific responses to IFN-{gamma}, TNF-, Clostridium difficile toxin B (TcdB), and ethylenediaminetetraacetic acid (EDTA). Endpoint immunofluorescence analysis of tight junctions and whole cell staining identified unique cellular features among treatment groups which significantly correlated with TEER, including cell-cell orientation (e.g., adjacent cell mean angle). ConclusionsWe demonstrate a high-throughput in vitro model of the homeostatic human colonic epithelium that maintains a long-term functional barrier. This model can be used to investigate complex epithelial barrier responses over time.

cell biology↗

bioRxiv2025

Accounting for longitudinal peak quality metrics with MSstats+ enhances differential analysis in proteomic experiments with data-independent acquisition

Mass spectrometry-based proteomics with data-independent acquisition benefits from advanced instrumentation and computational analysis. Despite continued improvements, the quality of quantification may be poor for some measurements. As the scale of proteomic experiments increases, these poor-quality measurements are challenging to characterize by hand, yet they undermine the detection of differentially abundant proteins and the downstream biological conclusions. We introduce MSstats+, a computational workflow that takes as input not only peak intensities reported by tools such as Spectronaut, but also quality metrics such as peak shape and retention time, and longitudinal run order profiles of these metrics. MSstats+ translates these metrics into a single measure of quality, and downweights poor quality measurements when detecting differentially abundant proteins. The method offers a natural treatment of missing values, weighting the imputed values according to the quality metrics in the run. We demonstrate the accuracy of the resulting differential analysis in four experiments: two custom benchmarking studies with intentionally induced anomalies, a controlled mixture of proteomes, and a large-scale clinical investigation. MSstats+ is implemented in the family of open-source R/Bioconductor packages MSstats.

bioinformatics↗

bioRxiv2025

Mapping mesenchymal diversity in the developing human intestine and organoids

The organization of diverse mesenchymal populations during human intestinal development is critical for tissue architecture and function yet remains poorly defined. To construct a comprehensive, tissue-scale map of the developing human small intestine, we leveraged single-cell RNA-sequencing data to build a custom Xenium spatial transcriptomics gene panel covering the diversity of cell types in the human intestine. Analysis was focused on the developing mesenchyme populations (also referred to as fibroblasts or stroma) given the lack of spatiotemporal information about these cell populations. We defined 5 broad mesenchymal populations occupying discrete anatomical locations within the lamina propria and submucosa - the subepithelial cells (SEC), lamina propria fibroblasts (LPF), submucosal fibroblasts (SMF), smooth muscle cells (SMC), and CXCL13+ fibroblasts. Our data reveal dynamic spatial remodeling of fibroblast communities during development and establish molecular markers to distinguish these populations. We leverage this high-resolution atlas to benchmark pluripotent stem cell-derived human intestinal organoids and to demonstrate how this foundational resource can be used to dissect intestinal stromal signaling in a spatial manner, with broad implications for modeling development, regeneration, and disease. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/665939v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@46299borg.highwire.dtl.DTLVardef@55a014org.highwire.dtl.DTLVardef@180a104org.highwire.dtl.DTLVardef@1cb42b2_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA spatial atlas of fibroblast heterogeneity in the human intestine C_LIO_LIPopulations include subepithelial, submucosal, lamina propria, and CXCL13+ fibroblasts C_LIO_LIFibroblast position in the developing intestine is maintained into adulthood C_LIO_LIOrganoids largely recreate the spatial organization of the human intestine C_LI

developmental biology↗

bioRxiv2025

Genome-Wide Investigation of Transcription Factor Occupancy and Dynamics Using cFOOT-seq

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences. Notably, cFOOT-seq, combined with FootTrack analysis, predicts TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.

molecular biology↗

bioRxiv2025

Mind the Gap: An Embedding Guide to Safely Travel in Sequence Space

We present a hybrid approach combining a protein language model (pLM) with Monte Carlo (MC) sampling for generating enzyme mutants free of mutations deleterious for structural preservation. Given the amino acid sequence of the original enzyme and a set of residues for which the local environment should be conserved, i.e., the catalytic site, our approach generates mutants that differ vastly in the overall sequence while retaining the geometry of the conserved region, thereby representing promising candidates for further experimental screening. Unlike end-to-end deep learning approaches, whose results are harder to interpret and control, the use of a well-established, classic technique such as MC sampling allows us to easily interpret the generative process as the sampling of an energy landscape determined by the pLM. In turn, such an interpretation enables us to steer this generative process and control its outcome by making use of robust statistical mechanics concepts, e.g., temperature, thereby explicitly guaranteeing certain properties of the generated mutants. Given the increasing relevance of generative algorithms in the design and search for novel, optimised enzymes, we believe that our results constitute an important step for the future development of this class of techniques. To facilitate experimental verification, we finally provide hundreds of sequences for 13 different enzymes involved in catalytic processes ranging from carbon dioxide conversion to DNA replication.

synthetic biology↗

bioRxiv2025

Gene-embedding-based prediction and functional evaluation of perturbation expression responses with PRESAGE

Understanding the impact of genetic perturbations on cellular behavior is crucial for biological research, but comprehensive experimental mapping remains infeasible. We introduce PRESAGE (Perturbation Response EStimation with Aggregated Gene Embeddings), a simple, modular, and interpretable framework that predicts perturbation-induced expression changes by integrating diverse knowledge sources via gene embeddings. PRESAGE transforms gene embeddings through an attention-based model to predict perturbation expression outcomes. To assess model performance, we introduce a comprehensive evaluation suite with novel functional metrics that move beyond traditional regression tasks, including measures of accuracy in effect size prediction, in identifying perturbations with similar expression profiles (phenocopy), and in prediction of perturbations with the strongest impact on specific gene set scores. PRESAGE outperforms existing methods in both classical regression metrics and our novel functional evaluations. Through ablation studies, we demonstrate that knowledge source selection is more critical for predictive performance than architectural complexity, with cross-system Perturb-seq data providing particularly strong predictive power. We also find that performance saturates quickly with training set size, suggesting that experimental design strategies might benefit from collecting sparse perturbation data across multiple biological systems rather than exhaustive profiling of individual systems. Overall, PRESAGE establishes a robust framework for advancing perturbation response prediction and facilitating the design of targeted biological experiments, significantly improving our ability to predict cellular responses across diverse biological systems.

bioinformatics↗

bioRxiv2025

Desiccation-induced DNA damage facilitates drug resistance in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is an obligate human pathogen that depends on its ability to spread from host-to-host to survive as a species. Yet, knowledge of transmission-specific traits remains lacking. Here, we report the discovery of a specific adaptive response to desiccation, a stress intrinsically linked to the generation of the aerosol droplets within which Mtb transmits. We show that desiccation inflicts oxidative damage and activates Mtbs DNA repair responses but that this repair is imperfect and results in mutations. We further show that activation of these DNA repair responses is accompanied by increased expression of the transcription-coupled repair factor, mfd, but that this expression serves to buffer the fitness cost of specific resistance-conferring mutations in rpoB, the target of the frontline drug rifampin, rather than to facilitate transcription-coupled DNA repair. Silencing mfd during aerosolization impairs survival of strains harboring the rifampin resistance allele S450L. This function is further supported by whole genome sequence data from over 50,000 clinically circulating strains. These studies indicate that Mtb has evolved transmission-specific stress responses that have enabled it to leverage desiccation-induced DNA damage as a potential source of genetic diversification and drug resistance.

microbiology↗

bioRxiv2025

LIN28A-Dependent Kinome and Phosphoproteome Reprogramming Promotes Imatinib Resistance

Chronic myeloid leukemia (CML) resistance to BCR-ABL tyrosine kinase inhibitors (TKIs) can arise from ABL kinase domain mutations, BCR-ABL fusion gene amplification, or kinase-independent mechanisms. To investigate imatinib-resistance, we performed quantitative mass spectrometry comparing the proteome and phosphoproteome of K562 cells (a standard CML model) and ImR cells, an imatinib-resistant K562 derivative that also exhibits cross-resistance to second- and third-generation BCR-ABL TKIs. In addition to revealing global proteome and phosphoproteome changes associated with drug resistance, we identified LIN28A--a multi-functional RNA-binding protein--as a critical mediator of imatinib resistance. LIN28A was significantly overexpressed and hyperphosphorylated in ImR cells. Depleting LIN28A via shRNA restored imatinib sensitivity, while its ectopic expression in parental K562 cells induced imatinib resistance. Mechanistically, LIN28A coordinates an extensive kinase-substrate network regulating proliferation, survival, and metabolism to drive resistance. Notably, pharmacological inhibition of LIN28A-dependent kinases (PKC, AKT, SGK1, and RPS6K) suppressed ImR proliferation. Midostaurin, a clinical PKC/FLT3 inhibitor used in FLT3-ITD--positive AML, potently re-sensitized ImR cells to imatinib. Our findings suggest that targeting LIN28A and its downstream effectors, particularly PKC, could overcome resistance to imatinib and next-generation BCR-ABL inhibitors.

cancer biology↗

bioRxiv2025

Mechanistic modeling identifies emergent properties of tandem SH2 domain interactions

In signaling cascades, where domain-motif interactions tend to interact with relatively low affinity (allowing for reversibility), signaling proteins often encode multiple domains or motifs. This presents the possibility for avidity where multivalent binding drastically increases the interaction strength and duration. However, given the large combinatorial space, predicting and validating multivalent interactions that interact with avidity is a challenge. Here, we integrate mechanistic modeling, structure-based analysis, and experimental approaches as a framework for defining the conditions under which avidity plays a role. We explore the tandem SH2 domain family of interactions with bisphosphorylated partners as a multivalent archetype, which encompasses key secondary messengers in tyrosine kinase signaling networks. While certain multivalent interactions have been shown to be necessary in immune receptor recruitment of partners, bivalent recruitment of tandem SH2 domains more broadly is poorly understood. Theoretical modeling suggests that maximum avidity occurs with closely spaced tyrosine phosphorylation sites combined with moderate monovalent affinities - exactly around the innate range of SH2 domain affinity - or with phosphorylation sites separated by sufficiently flexible linkers. Surprisingly, despite sequence diversity, structure-based analysis showed relatively conserved three-dimensional spacing between SH2 domains across all tandem SH2 families, which we corroborate experimentally, suggesting evolutionary optimization for avidity interactions. The combination of structure-based analysis of domain spacing with available monovalent experimental data appears, along with iterative experimental refinement of biophysical parameters, can identify high affinity interactions of tandem SH2 domain recruitment to the EGFR C-terminal tail. Using these principles, we extended bivalent predictions into the full phosphoproteome space and structural parameterization of other partners of SH2 domain binding, providing resources and methods for more rapid expansion of bivalent analysis. These approaches lay the groundwork for larger utility in multivalent prediction and testing to help better understand protein interactions that drive cell signaling.

molecular biology↗