Frontiers in Cell and Developmental Biology

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

Clonal hematopoiesis of indeterminate potential (CHIP) is an age-associated condition linked to chronic inflammation and an increased risk of cardiovascular diseases and hematological malignancies. People with HIV (PWH) exhibit a higher prevalence of CHIP than the general population, but the mechanisms underlying this association remain unclear. In particular, it is unknown whether the excess burden of CHIP reflects earlier emergence of mutant clones, altered clonal expansion dynamics, or differences in selective pressures acting on hematopoietic stem cells. We reconstructed longitudinal trajectories of CHIP variant allele frequency (VAF) in 52 PWH using serial peripheral blood samples spanning up to 25 years from the Swiss HIV Cohort Study. We used spline-based modelling to estimate clone size and growth dynamics, and dynamic time warping to identify common trajectory patterns. Associations between clonal dynamics and longitudinal immune parameters were assessed using linear mixed-effects models. Trajectories in PWH were compared with publicly available longitudinal CHIP data from the SardiNIA population cohort. We identified heterogeneous clonal dynamics consistent with known gene-specific fitness patterns. Larger clone size was associated with lower CD4 T-cell count and lower CD4/CD8 ratio. Compared with the general population cohort, PWH showed higher VAF across the observed age range and steeper early trajectory increases, while long-term expansion rates were broadly similar. Greater variability in clonal dynamics among PWH suggests a stronger contribution of host environmental factors to clonal fitness. These findings support a model in which HIV-associated immune dysregulation alters the hematopoietic fitness landscape, contributing to earlier detectable clonal expansion and increased burden of CHIP in PWH.

Altered iron homeostasis has long been implicated in Parkinson's Disease (PD), although the mechanisms have not been clear. Given the critical role of PD-related activating mutations in LRRK2 (leucine-rich repeat protein kinase 2) within membrane trafficking pathways we examined the impact of a homozygous mutant LRRK2G2019S on iron homeostasis within the RAW macrophage cell line with high iron capacity. Proteomics analysis revealed a dysregulation of iron-related proteins in steady state with highly elevated levels of ferritin light chain and a reduction of ferritin heavy chain. LRRK2G2019S mutant cells showed efficient ferritinophagy upon iron chelation, but upon iron overload there was a near complete block in the degradation of the ferritinophagy adaptor NCOA4. These conditions lead to an accumulation of phosphorylated Rab8 at the plasma membrane, which is selectively inhibited by LRRK type II kinase inhibitors. Iron overload then leads to increased oxidative stress and ferroptotic cell death. These data implicate LRRK2 as a key regulator of iron homeostasis and point to the need for an increased focus on the mechanisms of iron dysregulation in PD.

Background: Patient-ventilator synchrony, an essential prerequisite for non-invasive mechanical ventilation, requires an accurate matching of every phase of the respiration between patient and the ventilator. Methods: We developed a long short-term memory (LSTM)-based model that can predict the inspiratory and expiratory time of the patient. This model consisted of two hidden layers, each with eight LSTM units, and was trained using a dataset of approximately 27000 of 500-ms-long flow signals that captured both inspiratory and expiratory events. Results: The LSTM model achieved 97% accuracy and F1 score in the test data, and the average trigger error was less than 2.20%. In the first trial, 10 volunteers were enrolled. In "Compliance" mode, 78.6% of the triggering by the LSTM model was compatible with neuronal respiration, which was higher than Auto-Trak model (74.2%). Auto-Trak model performed marginally better in the modes of pressure support = 5 and 10 cmH2O. Considering the success in the first clinical trial, we further tested the models by including five patients with acute respiratory distress syndrome (ARDS). The LSTM model exhibited 60.6% of the triggering in the 33%-box, which is better than 49.0% of Auto-Trak model. And the PVI index of the LSTM model was significantly less than Auto-Trak model (36.5% vs 52.9%). Conclusions: Overall, the LSTM model performed comparable to, or even better than, Auto-Trak model in both latency and PVI index. While other mathematical models have been developed, our model was effectively embedded in the chip to control the triggering of ventilator. Trial registration: Approval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

Ewing sarcoma (EwS) is an aggressive, human-exclusive tumor typically driven by the EWS::FLI1 fusion protein. To assess whether the neomorphic functions of EWS::FLI1 are fundamentally dependent on evolutionarily recent cofactors such as ETS transcription factors (ETS-TFs), Plycomb group (PcG) proteins, CBP/p300, or specific subunits of the BAF complex, we expressed EWS::FLI1 in the model organism Saccharomyces cerevisiae. This minimal system was chosen because several key EWS::FLI 's cofactors possess greatly reduced sequence homology (e.g., BAF) or are lacking altogether (e.g., ETS-TFs, PcG, or CBP/p300). We used co-IP/MS to map the yeast interactome, Chip-Seq to identify gDNA binding sequences, RNA-Seq for global gene expression, and engineered reporters to test conversion of (GGAA) tandem repeats (GGAASat) into neoenhancers. We found that the yeast EWS::FLI1 interactome was more limited and qualitatively distinct from its human counterpart, sharing core machinery (e.g. RNA Polymerase II, FACT) but lacking the BAF/SWI-SNF and spliceosome complexes, and showing strong enrichment for the SAGA chromatin remodeling complex. We also found that EWS::FLI1 binds to hundreds of sites in the yeast genome with a clear preference for putative ETS-TF consensus sequences and (CA) dinucleotide repeats. Yet, EWS::FLI1 expressing cells presented only minimal transcriptional dysregulation, a stark contrast to the extensive changes observed in humans and Drosophila cells. Finally, we found that EWS::FLI1 successfully converted silent GGAASat sequences into active enhancers in yeast. This remarkable result occurs despite the absence of homologs for key human activators, such as CBP/p300, strongly suggesting that EWS::FLI1 can mobilize functionally related, non-homologous pathways to establish neoenhancers at GGAASat sites. Altogether, our results indicate that EWS::FLI1's core ability to drive GGAASat-dependent gene expression is a conserved, ancient property, while GGAASat-independent extensive transcriptome reprogramming is dependent on co-factors and pathways specific to animal cells.

Orphan genes, lacking homologs in other species, are systematically found across genomes. Their presence may result from extensive divergence from pre-existing genes or from de novo gene birth, which occurs when a gene emerges from a previously non-genic region. In this study, we identified orphan genes in the genomes of globally distributed plant-parasitic nematodes of the genus Meloidogyne and investigated their origins, evolution, and characteristics. Using a comparative genomics framework across 85 nematode species, we found that 18% of Meloidogyne genes are genus-specific, transcriptionally supported orphans. By combining ancestral sequence reconstruction and synteny-based approaches, we inferred that 20% of these orphan genes originated through high divergence, while 18% likely emerged de novo. Proteomic and translatomic evidence confirmed the translation of a subset of these genes, and feature analyses revealed distinctive molecular signatures, including shorter length, signal peptide enrichment, and a tendency for extracellular localization. These findings highlight orphan genes as a substantial and previously underexplored component of the Meloidogyne genome, with potential roles in their worldwide parasitism.

Circulating tumor cells (CTCs), and especially CTC-clusters, are linked to poor prognosis and may reveal mechanisms of metastasis and treatment resistance. Therefore, developing unbiased methods for the functional characterization of CTCs in liquid biopsies is an urgent need. Here, we present an evaluation of multiplex imaging mass cytometry (IMC) to analyze CTCs in mice with human xenograft tumors. In a single-step process, IMC uses metal-labeled antibodies to simultaneously detect a large number of proteins/modifications within minimally manipulated small volumes of blood from the tail vein or heart. We used breast cancer cell lines and a patient-derived xenograft (PDX) to assess antibodies for cross-species interpretation. Along with manual verification, HALO-AI-based cell segmentation was used to identify CTCs and quantify markers. Despite some limitations regarding human-specificity, this technology can be used to investigate the effect of genetic and pharmacological interventions on the properties of single and cluster CTCs in tumor-bearing mice.

End-stage liver disease (ESLD) is one of the leading causes of death worldwide. Currently, the only curative option for patients with ESLD is liver transplantation. However, the demand for donor livers far exceeds the available supply, partly because many potentially viable livers are discarded following biopsy evaluation. While biopsy is the gold standard for assessing liver histological features related to graft quality and transplant suitability, it often leads to high discard rates due to its susceptibility to sampling errors and limited spatial coverage. Besides, biopsy is invasive, time-consuming, and unavailable in clinical facilities with limited resources. Here, we present an AI-assisted photoacoustic/ultrasound (PA/US) imaging framework for quantitative assessment of human donor liver graft quality and transplant suitablity at the whole-organ scale. With multimodal volumetric PA/US images as the input, our deep-learning (DL) model accurately predicted the risk level of fibrosis and steatosis, which indicate the graft quality and transplant suitability, when comparing with true pathological scores. DL also identified the imaging modes (PAI wavelength and B-mode USI) that correlated the most with prediction accuracy, without relying on ill-posed spectral unmixing. Our method was evaluated in six discarded human donor livers comprising sixty spatially matched regions of interest. Our study will pave the way for a new standard of care in organ graft quality and transplant suitability that is fast, noninvasive, and spatially thorough to prevent unnecessary organ discards in liver transplantation.

Background and Aims: Alterations in immunoglobulin G (IgG) N-glycosylation are implicated in inflammatory bowel disease (IBD); however, the robustness of IgG glycan signatures across IBD cohorts with diverse demographics and geographic origins remains underexplored. We aimed to determine whether compositional data analysis (CoDA) and machine learning (ML) can identify IBD-related IgG N-glycan signatures and whether these signatures capture disease-associated acceleration of biological aging. Methods: We analyzed the IgG glycome profiles of 1,367 plasma samples collected from healthy controls (HC), symptomatic controls (SC), and people with newly diagnosed Crohn's (CD), and ulcerative colitis (UC) across four cohorts (UK, Italy, United States, and Netherlands). IgG glycosylation was analyzed by ultra-high-performance liquid chromatography, yielding 24 total-area-normalized glycan peaks (GPs). Analyses were performed using cross-sectional data obtained at baseline. CoDA-powered association analyses were used to identify disease-related effects on GPs while controlling for demographic covariates. ML models were trained and evaluated to assess generalizability to unseen cohorts and demographic subgroups, with a focus on discrimination and reliability. Results: Across all cohorts, people with IBD demonstrated accelerated biological aging as quantified by the GlycanAge index. This was accompanied by consistent reductions in IgG galactosylation, with effects partially modulated by age. Classification models trained on glycomics and demographics achieved robust discrimination (AUROC~0.80) between non-IBD (HC+SC) and IBD across cohorts. Conclusion: These findings reveal accelerated biological aging in people with IBD and support the translational potential of IgG glycans as biomarkers and a novel route toward clinically interpretable personalized risk estimates.

BackgroundPleuroparenchymal fibroelastosis (PPFE) is a rare, fibrotic lung disease with poor prognosis, usually affecting adults which most commonly occurs idiopathically. Biallelic pathogenic variants in DGUOK cause mitochondrial DNA (mtDNA) depletion syndrome, predominantly affecting infants with severe hepatic and neurological symptoms. Detailed description of pulmonary manifestations with late-onset presentation have not been reported. MethodsWe describe nine patients with PPFE and DGUOK-associated mitochondriopathy. Clinical, radiological, histopathological, and genetic data were systematically collected from all patients. Functional studies, single nucleus RNA sequencing (snRNAseq), immunofluorescence staining, transmission electron microscopy and respiratory chain enzyme activity assays were conducted on patient-derived fibroblasts, muscle or lung tissues. mtDNA content quantification was performed on whole genome sequencing (WGS) data. ResultsAll patients (ages 5-36) presented with progressive dyspnea, weight loss and some with spontaneous pneumothoraces. Chest computed tomography and lung biopsies showed features of PPFE. Biallelic pathogenic DGUOK variants were identified in all patients, seven of them carry an unreported intronic variant leading to mtDNA depletion. snRNAseq of lung tissue from four pediatric patients identified Aberrant Basaloid cells and intermediate cells as their precursor localized at the fibrotic edge. Mitochondrial alterations were identified by electron microscopy. ConclusionPPFE in children and young adults is associated with DGUOK-related mitochondriopathy. For the first time, we demonstrate Aberrant Basaloid cells in pediatric fibrotic lung tissue. Since pulmonary involvement may be underrecognized or misinterpreted and the clinical presentation may not always be typical of a mitochondriopathy, we recommend genetic testing in all patients with PPFE of unknown origin.

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, where a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential for faithfully capturing the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.

Patients who participate in intracranial neuroscience research make invaluable contributions to our understanding of the brain, accelerating the development of neurotechnological interventions. Engagement of patients as part of this research presents unique challenges, where study goals can be distant from immediate clinical applications and require specialized domain knowledge. Yet methods for meaningfully integrating patient communities as part of these research efforts is essential, as intracranial neuroscience guides the application of artificial intelligence for understanding and enhancing human cognition. In order to identify what patients consider meaningful research engagement we interviewed individuals who participated in a study during their Deep Brain Stimulation (DBS) surgery and attended a group event where they interacted with our research team. Analysis of semi-structured interviews identified four main themes: interest in science and the future of clinical care, contributing to science to improve lives, connecting with others, and accessibility considerations. Based on these insights, we propose strategies for transformational participation of patient communities in intracranial neuroscience research with respect to engagement objectives, communication and scope. This approach offers a foundation for sustaining relationships between scientists and communities rooted in trust and transparency, to ensure that impacts of neurotechnology on human health and cognition are aligned with patient needs as well as desired public values.

PurposeIdiopathic hypogonadotropic hypogonadism (IHH) is characterized by impaired reproductive maturation, and approximately half of all cases lack an identified genetic cause. We investigated the genetic basis of IHH in two large cohorts to identify novel disease-causing genes. MethodsWe analyzed exome and genome sequencing data from 1,822 patients with IHH from two independent cohorts. Rare variants were filtered using pedigree-informed inheritance models. PLEKHA6 expression in the postmortem human hypothalamus were tested at the mRNA and protein level. Functional studies assessed kisspeptin secretion in cell-based assays. ResultsWe identified 18 distinct PLEKHA6 variants in 24 patients from 20 unrelated families (1.3% of cohort). Variants segregated with disease under autosomal recessive and autosomal dominant (with variable penetrance) inheritance patterns. PLEKHA6 was robustly expressed in the hypothalamus and showed clear colocalization with neurokinin B, which served as the marker for the GnRH pulse generator. Functional studies demonstrated that patient variants significantly impaired kisspeptin secretion. ConclusionPLEKHA6 is a novel IHH gene and the first reported regulator of kisspeptin secretion from the kisspeptin-neurokinin B-dynorphin (KNDy) neurons, which have recently been established as the GnRH pulse generator. These findings establish impaired kisspeptin release as a new disease mechanism in IHH and highlight the critical role of neuropeptide trafficking in reproductive function.

BackgroundDespite scale-up of antiretroviral therapy (ART), children living with HIV (CLHIV) and children who are HIV-exposed-but-uninfected (CHEU) are at an increased risk of poor growth outcomes compared to children HIV-unexposed-and-uninfected (CHUU). Few studies quantify the magnitude of growth deficits extending into school age in sub-Saharan Africa (SSA). This study examined the impact of perinatal HIV exposure and infection on the growth trajectory of school-aged children in Nigeria. MethodsWithin a prospective cohort, 569 children aged 3-11 years were recruited from pediatric clinics in Nigeria and matched by age and sex based on their exposure or infection status. School-aged children were observed across three time-points at 6-month intervals, during which anthropometric measures, CD4 count, and maternal factors were collected. Z-scores for height-for-age (HAZ), weight-for-age (WAZ), and body-mass-index-for-age (BAZ) were calculated using WHO standards. Longitudinal linear regression analyses using generalized estimating equations (GEE), adjusted for maternal and child covariates, were conducted to compare growth outcomes across groups. ResultsGrowth Z-scores declined until approximately age 8, after which they gradually increased. Across all visits, CLHIV consistently and independently demonstrated lower Z-scores (WAZ ({beta} = -1.04, p <0.001); HAZ ({beta} = -0.67, p <0.001)), followed by CHEU with intermediate but significant impairments (WAZ ({beta} = -0.35, p <0.01); HAZ ({beta} = -0.38, p <0.01)) compared to CHUU. ConclusionStunting remains unacceptably high in CLHIV and CHEU in SSA. The findings suggest a need for immediate paradigm shifts to address persistent growth deficits despite ART and beyond infancy.

Female genital tract (FGT) polyps are common benign growths affecting up to half of all women. However, they carry malignant potential, and their genetic architecture remains poorly defined. We conducted a genome-wide association study (GWAS) meta-analysis across four biobanks (48,400 cases, 477,134 controls), identifying 26 risk loci for FGT polyps, 12 of which were previously unreported. Integrative gene prioritisation highlighted 193 candidate genes, revealing a potential convergent biological mechanism: where germline variation in DNA replication and maintenance (e.g., PRIM1, TERT and HMGA1) compromises genomic stability in the context of hormone-driven proliferation (e.g., ESR1 and GREB1). This susceptibility is further modulated by metabolic drivers of estrogen biosynthesis, underscored by specific adiposity-related loci (e.g. RSPO3 and PLCE1) and the aromatase gene CYP19A1. Mendelian randomisation demonstrated bidirectional causal relationships with endometriosis and fibroids, and endometrial cancer. Leveraging the shared genetic architecture of FGT polyps and other gynaecological disorders via multi-trait analysis revealed an additional 26 loci, validating sub-threshold regions encompassing HMGA1 and GREB1. In total, 52 risk loci were identified (36 novel), 39 of which replicated in an independent cohort. These findings reframe polyps not merely as local gynaecological overgrowths but as manifestations of a systemic proliferative syndrome characterised by dysregulated genome stability and estrogen signalling, which may also impact malignant transformation.

Irritable Bowel Syndrome (IBS) affects a substantial proportion of university students, yet its factors remain incompletely characterised in South Asian populations. We reanalysed a publicly available dataset of 550 Bangladeshi students from Hasan et al. (2025), conducting a data audit that identified implausible records, including males reporting menstrual symptoms, and reduced the analytic sample to 506 observations. Using Explainable Boosting Machines (EBMs), which capture non-linear effects and pairwise interactions without sacrificing interpretability, we found that psychological distress, elevated BMI and academic dissatisfaction were the strongest predictors of IBS (mean AUC = 0.852 across 100 stratified train-test splits). Critically, several findings diverged from the original logistic regression analysis. Physical activity showed a non-linear risk pattern only at high intensity, the association with gender was substantially weaker when we accounted for metabolic and psychological factors as well and malnourishment does not have a strong an impact as in the original study. These divergences likely arise because the machine-learning model captures non-linear effects and interactions that were not represented in the original regression specification. Our findings underscore the value of reanalysing existing datasets with methods suited to capturing complexity and highlight data quality verification as a necessary step in the secondary analysis.

COVID-19 has spread rapidly and caused a global pandemic making it one of the deadliest in history. Early identification of patients with coronavirus disease 2019 who may develop critical illness is of immense importance. Therefore, novel biomarkers were needed to identify patients who will suffer rapid disease progression to severe complications and death. Many treatments were adopted including the antiviral Remdesivir, the antiretroviral Lopinavir /Ritonavir and Tocilizumab. Our study aimed not only to specify high-risk factors and biomarkers of fatal outcome in hospitalized subjects with coronavirus but also to compare the efficacy of the three considered treatments to help clinicians better choose a therapeutic strategy and reduce mortality. We divided the population (n=711) into four main groups based according to the WHO ordinal severity scale. The percentage of mortality, in and out the hospital, the length of stay in the hospital, the pulmonary inflammatory lesion and its distribution, the SARS-CoV-2 IgM and IgG variations at admission, the inflammatory markers, the complete blood count, the coagulation factors and enzymes, proteins and electrolytes profile, glucose and lipid profile, and other relevant markers were measured. The significance of the observed variation was assessed by multivariate and ANOVA analyses. We succeeded to establish a novel predictive scoring model of disease progression based on a cohort of Lebanese hospitalized patients relying on the pulmonary inflammatory lesions, inflammation biomarkers such as LDH, D-Dimer, CRP, IL-6 and the lymphocyte count, the number of comorbidities and the age of the patient which all were significantly correlated with the illness severity showing best outcomes with immunomodulatory and anticoagulant treatments by the results. As top tier, Tocilizumab was more efficient than the two other treatments in non-severe cases but none of the used treatments was insanely effective alone to reduce mortality in severe cases.

Systemic inflammation is implicated in various diseases, yet its upstream determinants remain poorly examined. We conducted a large scale two-sample Mendelian randomisation (MR) study to systematically evaluate the potential causal effects of 3,213 molecular (metabolomic, proteomic), physiological and disease traits on circulating interleukin-6 (IL-6) and C-reactive protein (CRP) levels. Genetic instruments were derived from genome wide association studies and analysed using inverse variance weighted (IVW), weighted median, and MR-Egger methods with multiple testing correction. Bidirectional MR was performed to assess reverse causation. After Bonferroni correction, evidence of potential causal effects was observed for 72 traits on CRP and 9 traits on IL-6. CRP was predominantly influenced by metabolomic traits, especially lipid and fatty acid measures. Genetically proxied adiposity (body mass index and obesity), triglyceride rich lipoproteins, glycoprotein acetyls (GlycA), and apolipoprotein E increased CRP levels, whereas HDL-related cholesterols, polyunsaturated fatty acids, and glutamine decreased CRP. Most associations were consistent across MR methods, supporting the robustness of these results. As expected, IL-6 had a large effect on CRP. IL-6 was influenced by primarily adiposity and HDL-related lipid measures, with generally smaller effect sizes and limited support across sensitivity analyses. Bidirectional analyses indicated little evidence that CRP directly drives metabolic traits when restricting to cis-acting instruments, whereas genetically proxied IL-6 signalling showed consistent downstream effects on HDL particle concentration and composition. Adiposity is a shared upstream determinant of both inflammatory biomarkers, with stronger and broader effects on CRP. These findings suggest that CRP acts as an integrated downstream readout of systemic inflammatory burden, whereas IL-6 reflects a more tightly regulated and context-dependent process. Our work clarifies traits that may causally influence systemic inflammation and highlights biological pathways linking inflammation to cardiometabolic and inflammatory diseases. By mapping upstream determinants of IL-6 and CRP, we also provide a resource to prioritise key drivers for mechanistic study and therapeutic targeting.

BackgroundDespite extensive characterization of key oncogenic drivers, pancreatic ductal adenocarcinoma (PDAC) continues to exhibit profound molecular heterogeneity and inconsistent responses to standard therapies, including gemcitabine. The role of pathway-level alterations, particularly in the context of age at onset and therapeutic exposure, remains insufficiently defined. MethodsIn this study, we leveraged a conversational artificial intelligence framework (AI-HOPE-TP53 and AI-HOPE-PI3K) to enable precision oncology, driven interrogation of clinical and genomic data from 184 PDAC tumors, stratified by age at diagnosis and gemcitabine exposure. Using AI-enabled cohort construction and pathway-centric analyses, we evaluated alterations in TP53 and PI3K signaling networks, with findings validated through conventional statistical methods. ResultsTP53 pathway analysis revealed a significantly higher frequency of TP53 mutations in early-onset compared to late-onset PDAC among gemcitabine-treated patients (86.7% vs. 57.1%, p = 0.04), with a similar trend observed between treated and untreated early-onset cases (86.7% vs. 40%, p = 0.07). Notably, in late-onset PDAC patients not treated with gemcitabine, absence of TP53 pathway alterations was associated with improved overall survival (p = 0.011). Complementary analyses of the PI3K pathway demonstrated a higher prevalence of pathway alterations in late-onset gemcitabine-treated tumors compared to untreated counterparts (13.2% vs. 2.7%, p = 0.02). Importantly, among late-onset patients not receiving gemcitabine, those without PI3K pathway alterations exhibited significantly improved overall survival (p < 0.0001). ConclusionTogether, these findings identify distinct TP53 and PI3K pathway dependencies that are modulated by both age-of-onset and treatment exposure in PDAC. This work highlights the utility of conversational artificial intelligence in enabling rapid, integrative, and hypothesis-generating analyses within a precision oncology framework, supporting the identification of clinically relevant molecular stratification strategies for this aggressive disease.

Mycetoma is a neglected tropical disease caused by various bacterial and fungal pathogens that has a significant health impact across a broad geographically defined "mycetoma belt" spanning South America, Africa and Asia. Histologically, mycetoma is characterised by invasive and destructive granuloma development in the skin, deep tissues and bone, leading to tissue destruction, deformities and high morbidity. The presence of macroscopic, highly compacted pathogen microcolonies, or "grains," is a key diagnostic feature, and the formation of grains supports pathogen persistence and disease chronicity. However, there is a paucity of information on immune responses in mycetoma patients and on the relative importance of phylogeny and/or grains in establishing the local immune landscape. Here, we used spatial proteomics to examine the distribution of 43 immune-related proteins in surgical biopsies from 11 patients with mycetoma of bacterial (Actinomycetoma; Actinomadura pelletierii and Streptomyces somaliensis; n=6) and fungal (Eumycetoma; Madurella mycetomatis; n=5) origin. Using mixed-effects modelling, an exploratory analysis across species and pathogen classes revealed few significant differences in immune marker expression. In contrast, and independently of pathogen class, the cellular infiltrate closest to grain boundaries had higher per-cell expression of CD66b+, ARG1, and VISTA. The preferential accumulation of CD66b+ARG1+VISTA+ cells at grain boundaries was confirmed by quantitative immunofluorescence analysis. Hence, the local tissue microenvironment surrounding the mycetoma grain represents a specialised immunosuppressive niche, with parallels to the tumour microenvironment.