Biology subjects

Long, M.

Publications and source records attributed to Long, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Cancer cells acquire cysteine directly from a reduced tumor microenvironment

The metabolic environment of a tumor is shaped by the nutrients available in its interstitial fluid, yet the redox composition of that fluid is poorly defined. Measuring sulfur metabolites in the interstitial fluid of tumors, matched healthy organs, and serum across three mouse models of cancer, we find that the tumor microenvironment (TME) is a chemically reduced compartment in which free cysteine predominates over its oxidized form, cystine. This is especially pronounced in the lung TME, where cystine is nearly absent. We further show that lung cancer cells take up cysteine and cystine with comparable efficiency and proliferate on cysteine alone, and that the abundant glutathione in the TME helps keep cysteine reduced through disulfide exchange. In a medium formulated to match the sulfur composition of the lung tumor interstitial fluid, cells become independent of cystine import through xCT. Cells cultured in this medium were resistant to glutaminase inhibitors, oxidants and ferroptosis inducers, and better maintained NADPH levels under oxidative challenge. These findings suggest that standard culture conditions overestimate the dependence of cancer cells on cystine uptake and their vulnerability to oxidative stress-targeting therapies.

cancer biology↗

bioRxiv2026

Localis-REX: a general protocol for time-resolved spatiofunctional profiling in live cells and C. elegans

We describe Localis-REX, a method for identifying organ or subcellular locale-specific innate reactive electrophilic metabolite (REM) responsive proteins in live Caenorhabdtis elegans and cultured cells. Localis-REX mimics the natural spatiotemporal production of REMs by expressing HaloTag transiently or stably in a specific organ or tissue of live worms or in a designated organelle or subcellular compartment in cultured cells. Following treatment with a submicromolar photocaged REM and washout, light-triggered uncaging generates brief, localized REM pulses near Halo. REM responsive proteins within the vicinity compete to capture the liberated REM before endogenous clearance processes, including glutathione adduction and redox metabolism, enabling selective identification of the most REM-responsive targets under minimally perturned conditions. Localis-REX can be coupled with 1) proteomics to map chemically-actionable local proteomes or 2) live cell and in vivo functional readouts to define biological responses to locale-specific REM-accumulation, such as translation efficiency monitoring through genetic code expansion. The workflow requires 3-6 h, with downstream data acquisition and analysis requiring an additional 24-48 h, depending on the application 1) or 2. We further describe integration of in vivo Localis-REX with ultraID spatial profiling in live C. elegans, enabling expression normalization across profiled locales. Mechanistic validation of newly-identified responder and REM pairs is facilitated using established T-REX and Z-REX target engagement and on-target REM signaling interrogation protocols. Notably, the workflow was initially established as part of a masters thesis project by a student co-author with no prior experience in C. elegans or cell culture research by the master student, highlighting its accessibility to well-trained junior researchers pursuing interdisciplinary studies.

systems biology↗

bioRxiv2026

Highly resolved tumor architecture via matched spatial and nucleus transcriptomics from a single tissue section

Spatial transcriptomics often relies on reference-based deconvolution to infer cell types in a tissue context; however, public single-cell datasets can miss patient-specific biology. Here we introduce SIMPlex, a method that generates matched spatial and single-nucleus gene-expression profiles from the same 5 um FFPE section. We demonstrate context-matched profiles across mouse brain, breast cancer and prostate cancer tissues, resolving fine-grained cell-states with distinct spatial signatures. By extracting both spatial and nuclear layers, SIMPlex maximises the information recovered from a single tissue section, an advantage for scarce archival and clinical specimens.

molecular biology↗

bioRxiv2026

Temporal decoding of blood flow derived mechanical cues driving liver regeneration

Liver regeneration is initiated by rapid vascular changes, yet how blood flow-derived mechanical cues are decoded by liver sinusoidal endothelial cells (LSECs) remains unclear. Here, we found that partial hepatectomy generates temporally distinct mechanical cues in vivo, with a transient rise in shear stress followed by progressive sinusoidal dilation and endothelial stretch. To dissect these forces, we developed a liver regeneration chip that reconstructs sinusoidal architecture and enables independent or coupled manipulation of shear stress and mechanical stretch. Shear-dominant, stretch-dominant, and coupled mechanical modalities induce divergent LSEC regenerative programs involving extracellular matrix remodeling, cell-cycle regulation, cytoskeletal organization, and angiocrine signaling. Mechanistically, force-specific pathways, including Wnt, HIF-1, NF-{kappa}B, and Piezo1-associated signaling, mediate these outputs. Inhibition of these pathways after partial hepatectomy impairs hepatocyte proliferation and survival. These findings reveal that LSECs temporally decode blood flow-derived mechanical forces into distinct regenerative outputs, establishing endothelial mechanotransduction as an upstream regulator of liver regeneration. HIGHLIGHTS{blacksquare} Partial hepatectomy decouples transient shear from progressive stretch in vivo. {blacksquare}A liver regeneration chip recreates structure and distinct mechanical modalities in sinusoids. {blacksquare}Distinct mechanical modalities encode divergent LSEC regenerative programs. {blacksquare}Force-specific LSEC mechanotransduction supports hepatocyte proliferation and survival.

bioengineering↗

bioRxiv2026

M1C IS NECESSARY FOR DARAXONRASIB RESISTANCE OF NSCLC KRAS(G12C) MUTANT CELLS

IntroductionThe RAS(ON) multi-selective daraxonrasib (RMC-6236) inhibitor is effective in patients with NSCLC KRAS mutant cancers. Tolerance to daraxonrasib invariably develops by mechanisms that remain unclear. There is no known involvement of the M1C oncogenic protein in daraxonrasib resistance. MethodsNSCLC H358 KRAS(G12C), H2122 KRAS(G12C) and patient derived MGH1112 KRAS(G12C) cells with acquired daraxonrasib resistance were investigated for M1C dependence in studies of SHP2, STAT1/3 and NF-KB activation, clonogenicity, and self-renewal capacity. ResultsWe demonstrate that M1C is induced as a protective response in NSCLC KRAS(G12C) mutant cells treated with daraxonrasib. We report that M1C forms novel cell membrane-associated biomolecular condensates with the SHP2 protein tyrosine phosphatase in driving daraxonrasib resistance. M1C integrates SHP2 activation with induction of (i) oncostatin-m/gp130/STAT3 signaling, and (ii) the NF-{kappa}B-mediated epithelial-mesenchymal transition (EMT) pathway. The functional significance of this M1C-driven pathway is supported by the demonstration that targeting STAT3 and NF-{kappa}B reverses daraxonrasib resistance. Consistent with M1C dependence, we also show that targeting M1C is effective against daraxonrasib-resistant NSCLC KRAS mutant cell line and tumor models. In contrast, M1C drives sotorasib resistance by STAT1-mediated inflammatory signaling, demonstrating that M1C confers resistance to KRAS(G12C)-selective and RAS(ON) tri-complex inhibitors by noncongruent mechanisms. ConclusionsThese findings demonstrate that M1C is required for daraxonrasib tolerance and is a potential target for the treatment of patients with NSCLC KRAS(G12C) mutant tumors refractory to this agent.

Cancer Biology↗

bioRxiv2026

Buffered sweat microfluidics with AI-enabled translation for clinically actionable blood urea estimation and renal risk stratification

Kidney-function assessment relies on blood urea as a clinically informative metabolic marker; however, its dependence on venipuncture and centralised laboratory testing limits high-frequency monitoring and delays timely clinical intervention. Here, we report an integrated platform combining a wearable buffered microfluidic patch with a physiology-informed, data-driven calibration framework for real-time, non-invasive estimation of blood urea from microlitre-scale sweat volumes (4.79 L). By precisely regulating the release kinetics of internal buffer salts, the device stabilises the local reaction microenvironment, mitigating variability in sweat pH and flow to ensure reproducible measurement. The resulting signals are processed through an artificial intelligence (AI)-enabled analysis pipeline that integrates sweat urea with patient-specific physiological information to generate clinically interpretable outputs. In multicentre studies, sweat urea shows a strong association with blood urea across diverse cohorts, but with nonlinear and time-lagged relationships that limit direct use. The AI-enabled calibration model compensates for these effects, enabling high-fidelity estimation of blood urea (r = 0.945 versus gold-standard measurements) at clinically relevant concordance levels. The platform further identifies kidney injury with 89.1% accuracy and stratifies disease severity with 83.2% accuracy. Notably, these results demonstrate that the integration of physicochemical stabilisation and AI-enabled data-driven translation establishes sweat as a clinically actionable surrogate for renal monitoring, supporting population-level estimation and highlighting the potential for personalised longitudinal assessment, and enabling a scalable, non-invasive strategy for high-frequency kidney disease management.

biophysics↗

bioRxiv2026

Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics

The spatial organization of tissues emerges from cell interactions across multiple scales, yet current spatial omics analysis tools often emphasize local neighborhoods and may not summarize broader tissue architecture. Here we introduce Cophenetic Spatial Topology Embedding (COSTE), a computational framework that embeds directed nearest-neighbor distance profiles into a hierarchical metric space without requiring the user to define a spatial radius or neighborhood cutoff. COSTE can be applied to cell-level and single-transcript inputs without requiring cell segmentation. It constructs directed distance profiles between cell populations and uses hierarchical clustering to quantify tissue topology. This yields a Spatial Separation Score (SSS), a sample-normalized score from 0 to 1 that summarizes relative spatial separation within an analyzed tissue. We apply COSTE to spatial transcriptomics datasets of pulmonary fibrosis and triple-negative breast cancer (TNBC), where it delineates tissue structures, nominates spatially defined cell states, and highlights disease-or treatment-associated architectural patterns that are not readily captured by local neighborhood-based analyses. Our approach provides an interpretable framework for exploring tissue architecture and cell-cell spatial relationships in spatial omics data.

bioinformatics↗

bioRxiv2026

Smartphone Placement Recognition during Walking: Performance Determinants and Real-World Generalizability

The opportunity to collect movement data from smartphones for prolonged periods has opened new perspectives in the field of clinical movement analysis. However, when monitoring peoples mobility in free-living conditions, smartphone placement can influence the validity of the extracted digital mobility outcome. This study aimed to develop and validate an automatic smartphone placement recognition classifier and to investigate potential critical factors that can influence performance. The classifier was trained on data from 15 healthy participants using inertial signals collected from smartphones placed at six body placements during free-living walking and externally validated on over 3,000 individuals from external datasets, including blind participants and patients with cardiovascular or Parkinsons disease. A decision-tree ensemble model was developed using feature subsets of increasing dimensionality, with the optimal subset comprising 50 features. Classification accuracy increased consistently when front and back pocket placements were aggregated (81.1%) and further improved when coat pocket was also included in the pocket class (88.5%), underscoring the challenge of distinguishing between fine-grained pocket placements. The best-recognized placements across the external datasets were lower back (precision: 100%, recall: 72.5%), hand (precision: 94.2%, recall: 94.5%), and the aggregated pocket class (precision: 86.7%, recall: 90.2%). Recognition accuracy changed across cohorts (0.73 - 0.85), activities (0.63 - 0.94) and speed (0.79 - 0.87), however it stayed consistent across various technological and environmental factors. Overall, this study demonstrates the feasibility of robust placement recognition in walking and underscores the importance of accounting for key influencing factors when designing frameworks intended for deployment in heterogeneous real-world or clinical contexts. HighlightsO_LIMachine learning accurately identifies smartphone placement during real-world gait C_LIO_LISix on-body placements recognized, including pockets, hand, bag, and lower-back C_LIO_LIFree-living data used for training, ensuring robust performance across conditions C_LIO_LIFeature selection and hyperparameter tuning optimize classification accuracy C_LIO_LIExternal validation confirms generalizability across >3,000 healthy and diseased adults C_LI

bioengineering↗

bioRxiv2026

PHGDH is a targetable driver of PDAC progression

Pancreatic ductal adenocarcinoma (PDAC) arises in a nutrient-deprived microenvironment through progressive stages from pancreatic intraepithelial neoplasia (PanIN) to invasive carcinoma. While serine metabolism supports tumor growth across multiple cancer types, the stage-specific role of de novo serine synthesis in PDAC evolution remains undefined. Here, we show that expression of phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme of serine biosynthesis, increases progressively from PanIN to invasive PDAC in human and mouse specimens. Using genetically engineered mouse models with inducible PHGDH knockdown, we found that PHGDH loss delayed PDAC development. Unexpectedly, PHGDH-deficient tumors did not increase reliance on exogenous serine, and dietary serine/glycine manipulation had no effect on tumor development. Instead, stable isotope tracing and metabolomic profiling revealed that PHGDH loss suppressed mTOR signaling, reduced expression of the glutamine transporter ASCT2, and impaired glutamine uptake and utilization. Leveraging this metabolic liability, we demonstrated that PHGDH-deficient tumors exhibited selective sensitivity to the glutamine antagonist DRP-104, whereas PHGDH-intact tumors were resistant. These findings reveal an unanticipated connection between serine biosynthesis and glutamine metabolism in PDAC and identify a therapeutic vulnerability that may be exploited through combined metabolic targeting. Statement of significancePHGDH supports PDAC progression not primarily through serine provision, but by maintaining glutamine metabolism and mTOR signaling. This unanticipated metabolic crosstalk creates a synthetic lethal vulnerability to glutamine antagonism in PHGDH-deficient tumors, providing a rationale for combining serine synthesis pathway inhibitors with glutamine-targeting therapies in pancreatic cancer.

cancer biology↗

bioRxiv2025

A simple, anesthesia-free infusion technique for in vivo metabolic tracing

Accurate metabolic flux analysis requires tracer delivery that preserves physiological metabolism. Current methods may distort metabolism through anesthesia, surgical stress, or complex procedures. We demonstrate that isoflurane anesthesia profoundly alters serum and tissue metabolism across multiple pathways. Glycolytic and TCA cycle intermediates, sulfur and aromatic amino acid metabolites, acylcarnitines, and nucleotide pools decreased, while branched-chain amino acids, their ketoacids, ketone bodies, and fatty acids increased. These coordinated changes were suggestive of mitochondrial complex I inhibition and reduced oxidative catabolism, leading to shifts in metabolite pool sizes that compromise isotopologue-based flux interpretation. We established a tail vein catheterization method completed in minutes under brief anesthesia that enables multi-hour tracer infusion in awake, freely moving mice. This method achieved steady-state labeling of cystine and downstream products comparable to jugular infusion without supraphysiologic cystine accumulation. This platform provides a practical, physiologically accurate method for in vivo steady-state isotope tracing.

biochemistry↗

bioRxiv2025

Machine learning-based development of Gadolinium binding peptides

Gadolinium-based contrast agents (GBCAs) are indispensable tools in magnetic resonance imaging (MRI), yet their clinical use is limited by non-specific tissue accumulation, low molecular specificity, and safety concerns. Protein and peptide scaffolds provide a promising alternative because they can bind metal ions with high selectivity and enable precise molecular targeting. However, identifying short peptide motifs with optimal gadolinium (Gd3) coordination and high relaxivity remains a major challenge. Here, we used a machine-learning-driven peptide evolution platform, the Protein Optimization Engineering Tool (POET), to design and optimize short Gd-binding motifs that enhance longitudinal relaxivity (r). Two algorithmic strategies were tested; motif-based and regular-expression-based representations, both were trained on an initial set of 74 twelve-amino-acid peptides derived from natural EF-hand scaffolds. Through two rounds of directed evolution and experimental screening, we identified peptides with up to a 24% increase in r ratio compared with the best natural EF-hand, and a 55% improvement in absolute r after removal of unbound Gd. Further analysis revealed that peptides exhibiting higher relaxivity generally possessed a more negative net charge and lower isoelectric point than the buffer pH, indicating stronger electrostatic stabilization of Gd3. Sequence enrichment analysis showed that acidic and small polar residues, particularly aspartic acid, glycine, and threonine, were selectively favored during evolution, while bulky hydrophobic and basic residues were depleted. These compositional trends align with improved solubility and enhanced metal coordination. Together, these results demonstrate a generalizable framework that integrates computational evolution with biophysical screening to discover new biologically derived Gd-binding motifs. This approach provides a scalable route to engineer responsive, tunable, and biocompatible MRI contrast tags for precision imaging and molecular diagnostics.

biophysics↗

bioRxiv2025

Sex-biased duplicates are rapidly generated during Drosophila tRNA repertoire evolution

Eukaryotic genomes encode hundreds of transfer RNA genes ostensibly to ensure efficient translation. How these seemingly redundant tRNA copies arise and are naturally selected in metazoan genomes and populations remains largely unexplored, owing to challenges in accurate sequencing of tRNA loci and their transcripts. We leveraged long-read genome assemblies of 24 Drosophilid species to infer the origination times of the entire Drosophila melanogaster tRNA gene repertoire and found that continuous gain and loss of tRNA duplicates throughout 60 million years of divergence has resulted in rapid taxonomic restriction of isodecoding and isoaccepting copies--even producing one isodecoder specific to D. melanogaster. Moreover, we identified patterns of global codon usage, especially in lineage-specific genes, incongruous with translational efficiency hypotheses. Through generation of tRNA sequencing in D. melanogaster we observed that recently duplicated, taxonomically restricted tRNA copies had sexually dimorphic patterns of expression, fragmentation, and nucleoside modification. Our work implicates the emergence of taxonomically restricted tRNA genes as sources of regulatory diversity and reveals that sexual and natural selection affect the evolutionary dynamics of the tRNA gene family in metazoan species.

evolutionary biology↗

bioRxiv2025

Lower Airway Dysbiosis in NTM+ Bronchiectasis is Associated with NET-Predominant Severe Phenotypes

RationaleThe discoveries of neutrophilic inflammation and Pseudomonas-dominant pulmonary dysbiosis have helped pave the way for host-directed therapy in bronchiectasis. Substantial knowledge gaps remain about the interplay between neutrophilic signatures and microbes in non-tuberculous mycobacterial lung disease (NTM-LD), a phenotypically diverse lung infection that is increasingly prevalent in the United States and other parts of the world. ObjectivesEvaluate the lower airway microbiota and neutrophilic traits in NTM- and NTM+ bronchiectasis. Methods16S rRNA gene sequencing, cell counts and neutrophil extracellular trap (NET) immunoassays were performed on bronchoscopic lower airway samples in 200 bronchiectasis subjects (108 NTM-, 92 NTM+). A preclinical model of oral commensal micro-aspiration and NTM infection was used to profile the murine lower airways with flow cytometry and a NET assay. Measurements and Main ResultsLower airways of NTM+ bronchiectasis patients were enriched with Mycobacterium and oral commensals (e.g., Veillonella, Prevotella). NET levels were higher in NTM+ BAL. Mycobacterium and oral commensals co-occurred with NET and neutrophils in network studies. Distinct oral commensal taxa associated with severe disease phenotypes such as cavitary disease and exacerbators. In a murine micro-aspiration model, the combination of oral commensals and Mycobacterium led to a sustained pro-inflammatory immune response marked by an increase in Th17, {gamma}8T cells, PD-1+ T lymphocytes as well as higher NET levels. ConclusionsOur analyses showed that distinct microbiome features beyond the primary pathogen can contribute to neutrophilic inflammation and severe disease phenotypes in bronchiectasis/ NTM-LD.

immunology↗

bioRxiv2025

Total variation of quantitative phase map reveals cellular Young's modulus

Collective cell migration is a fundamental physiological process involved in wound healing, development, and tissue regeneration. Though the role of the environmental stiffness in collective cell migration has been extensively investigated, the effects of cellular stiffness have been less studied due to the lack of high-throughput in situ methods for characterizing cellular stiffness. Here, we characterize the cellular Youngs modulus in situ with a large field of view and in real time using use quantitative phase microscopy. We found that standard deviation and total variation of the phase are inversely related to the cellular Youngs modulus, while the total variation of phase has finer mechanical resolution. Integrating the total variation of phase with cell segmentation algorithms, we efficiently analyzed the cellular Youngs modulus for cells in a monolayer. Using this system, we found the cells at the wound frontier are much softer than that of the cells in the inner region of monolayer in in vitro wound healing assay. At single cellular level, the Youngs modulus of leader cells, boundary cells, and inner cells are significantly distinct from each other. To conclude, our method would help elucidate the essential role of cellular stiffness in collective cell migration and has the potential to further benefit the progress of mechanobiology.

biophysics↗

bioRxiv2025

Reversible therapeutic resistance in EGFR mutant lung cancer caused by RB1 loss induced lineage plasticity

Treatment with EGFR tyrosine kinase inhibitors (EGFR TKI) is standard of care for patients with lung cancer initiated by activating mutations in the EGFR gene. While EGFR TKI treatment is effective, virtually all patients progress on therapy. Loss of function mutations in the RB1 tumor suppressor gene are associated with poor EGFR TKI outcomes, but underlying mechanisms remain unclear. Analysis of genetically engineered experimental models and human clinical specimens described here demonstrates RB1 loss has minimal impact on initial cell cycle responses to EGFR TKI but markedly accelerates acquired resistance over time. RB1 loss promotes transcriptional plasticity that enables rare cells to spontaneously assume reversible, resistance-conferring lineage states. These findings highlight the importance of non-genetic adaptations in driving acquired EGFR TKI resistance, advance our understanding of how RB1 impacts cancer, and identify opportunities to improve EGFR TKI treatment outcomes by targeting reversible epigenetic resistance mechanisms.

cancer biology↗

bioRxiv2025

Identification of the negamycin split biosynthetic gene cluster in Kitasatospora purpeofusca ATCC21470

Negamycin is a ribosome-targeting antibiotic with activity against Gram-positive and Gram-negative bacteria including ESKAPE pathogens. Furthermore, it promotes premature stop codon readthrough. Its therapeutic potential is limited by low natural production and synthetic complexity. To enable scalable biosynthesis, we identified and characterized its genetic basis in Kitasatospora purpeofusca ATCC 21470. Two distant chromosomal regions, neg1 and neg2, were found to be essential. Deletion of neg1, involved in nitrite provision for N-N bond formation, reduced production to [~]10%, while deletion of neg2, which directs {beta}-lysine generation and scaffold assembly, abolished it completely. Isotope-labeling experiments confirmed nitrite incorporation. Transcriptomic and proteomic analyses further supported the involvement of both regions. The heterologous expression of neg1 along with the neg2 region in Streptomyces albidoflavus reconstituted negamycin biosynthesis, confirming the unusual involvement of two distant gene clusters in the biosynthesis, and provides a foundation for biotechnological production and further development of this promising antibiotic. SIGNIFICANCEThe rapid rise of antimicrobial resistance (AMR), particularly among Gram-negative ESKAPE pathogens, represents one of the most urgent global health threats. Despite this, the discovery and development of new antibiotics have stagnated. Addressing this challenge requires the exploration of natural products with novel mechanisms of action, alongside the development of scalable production strategies. Negamycin has emerged as a compelling candidate in this regard, characterized by an unusual mechanism of action and therapeutic potential extending beyond traditional antibacterial use. However, its development has been constrained by low production yields in the native producer. In this study, we identify and characterize the biosynthetic genes responsible for negamycin production, providing a foundation for pathway engineering, yield optimization, and the rational design of new analogs.

microbiology↗

bioRxiv2025

p38 blockade reverses the immune suppressive tumor microenvironment in metastatic breast cancer

Metastatic breast cancer (MBC) is a life-threatening disease with limited therapeutic options. The immune suppressive tumor microenvironment (TME) limits the potency of the antitumor immune response and facilitates disease progression and metastasis. Our current study demonstrates that p38 is a druggable target in the TME that regulates the outcome of the immune-tumor interaction. The study revealed that systemic blockade of p38 reduces metastasis, and this anti-metastatic response is negated by depletion of CD8+ T cells. Single-cell transcriptomic analysis of the immune-TME showed that pharmacological p38 inhibition (p38i) or tumor-specific inactivation of p38 by CRISPR/Cas9 (p38KO) resulted in a less exhausted and more activated CD8+ T cell phenotype. Immunophenotyping analyses demonstrated that p38 blockade reduced the expression of multiple inhibitory receptors on CD8+ T cells (i.e., PD-1, LAG-3, CTLA-4), indicating a reversal of immune exhaustion and enhanced immune activation systemically and in the TME. In contrast, p38 blockade did not exhibit inhibitory effects on T cells in proliferation assays in vitro and did not affect the proportion of regulatory T cells in vivo. The major negative impact of p38 blockade in vivo was on the myeloid populations, such as myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs). Further, tumor p38 activity was required for the expression of cytokines/chemokines and tumor-derived exosomes with high chemotactic capacity for myeloid cells. Altogether, this study highlights a previously unrecognized p38-driven pathway that promotes an immune suppressive TME and metastasis, and that therapeutic blockade of p38 has important implications for improving antitumor immunity and patient outcomes. STATEMENT OF SIGNIFICANCEThis study highlights a previously unrecognized p38-driven tumor pathway that promotes an immune suppressive microenvironment and metastasis, and that therapeutic blockade of p38 has important implications for improving antitumor immunity and patient outcomes.

cancer biology↗

bioRxiv2025

Nurr1 orchestrates claustrum development and functionality

Claustrum is the part of the forebrain known to be most widely interconnected tissue with almost all global regions. It is believed to be involved in coordinating multiple cognitive behaviors including consciousness formation. However, little is known about the molecular mechanisms underlying its development and involvement in behavioral control. Here we show that Nurr1 (Nr4a2) is the key transcription factor orchestrating claustral morphogenesis, functional connectivity and thus behaviors. Nurr1 deficient claustral cells aberrantly migrate into insular cortex, shaping claustrum into an abnormal wing-like structure, and ectopically turn on insular corticex genetic program. Accordingly, functional connectivity of the claustrum is not properly formed and relevant behaviors are dysregulated in Nurr1 deficient mice. We show that Nurr1 regulates claustral neuron positioning by suppressing G-protein signaling.

neuroscience↗