Biology subjects

Paun, A.

Publications and source records attributed to Paun, A..

7 recordsLinked to original sources

bioRxiv2026

Epithelial regulatory networks link non-coding genetic variation to clinical heterogeneity in ulcerative colitis

Ulcerative colitis (UC) is a genetically heterogeneous disease causing chronic intestinal inflammation. Genome-wide association studies have linked numerous non-coding single nucleotide polymorphisms (SNPs) to UC, yet how these variants relate to intestinal epithelial function and clinical outcomes remains largely unknown. To address this, we aimed to reconstruct patient-specific epithelial signalling and regulatory networks perturbed by non-coding SNPs and to dissect patient heterogeneity in a large UC phase III trial. We analysed genotype data from 452 participants in the etrolizumab phase III HICKORY cohort using the integrated SNP network platform (iSNP) to map the downstream effects of non-coding SNPs and identify transcription factors (TFs) they perturb in each patient. TFs were filtered to retain those with differential regulatory activity in UC epithelial cells, patients were stratified by these TF profiles, and clusters were correlated with etrolizumab outcomes. We identified 14 SNP-propagated TFs with differential epithelial regulatory activity. Unsupervised clustering stratified patients into subgroups differing in disease severity and treatment response: two clusters had the highest severity and lowest remission and response rates, whereas two others had the best outcomes. iSNP-based clustering improved endoscopic healing prediction (p = 0.03), with trends for clinical response and histological endoscopic mucosal improvement. This systems genomics framework links patient-specific non-coding SNPs to epithelial signalling and regulatory networks in UC and captures clinically meaningful heterogeneity associated with treatment outcomes. It supports genetics-informed precision medicine and improved clinical trial design in inflammatory bowel disease and beyond.

systems biology↗

bioRxiv2026

Single-cell analysis of an adult IBD INCEPTION cohort reveals Galectin-linked disease mechanisms

Background and AimsThe molecular pathogenesis of Inflammatory Bowel Disease (IBD) remains unclear. We aimed to establish a high-resolution immune landscape of treatment-naive IBD to identify central drivers of disease onset and early pathogenic signalling. MethodsWe generated a single-cell atlas using intestinal biopsies from a large adult inception cohort of 137 individuals, including treatment-naive Crohns disease (CD), ulcerative colitis (UC), and symptomatic non-IBD controls. We integrated scRNA-seq (1 million cells) with co-varying neighbourhood analysis (CNA) and unbiased tensor decomposition of cell-cell communication (CCC) networks. Findings were validated in vitro macrophage stimulation model and using serum from patients. ResultsThe inception cohort exhibited significantly more homogenous compartmental diversity compared to benchmark reference studies (p < 0.001). Inflammation in both CD and UC was characterized by a marked expansion of inflammatory monocytes. Unbiased CCC analysis identified a dominant disease-specific signalling module centred on the Galectin family (LGALS1 and LGALS9). Galectin-9 expression was specifically enriched in inflammatory monocytes, which exhibited distinct transcriptional programs linked to antigen presentation and microbial sensing. In vitro, Galectin-9 acted as a potent stimulus, driving macrophages toward a pro-inflammatory phenotype. Clinically, serum Galectin-9 levels were significantly elevated in IBD patients and correlated with systemic inflammatory markers and treatment response. ConclusionsOur data identify a galectin-monocyte signalling axis as a unifying inflammatory hallmark of early IBD. Galectin-9 serves as both a functional driver of mucosal inflammation and a dynamic biomarker, offering new opportunities for therapeutic targeting and disease monitoring from diagnosis.

immunology↗

bioRxiv2026

Label-Free Live Cell Type Prediction by Integrating Raman Spectroscopy and Machine Learning

Coherent Raman spectroscopy enables label-free biochemical fingerprinting of live cells with subcellular resolution. We previously developed a machine learning framework capable of classifying glioma FFPE tissues using Raman spectral signatures. To accelerate live cell acquisition, we previously developed RADAR (Raman Spectral Analysis Using Deep Learning for Artifact Removal), a method that increases imaging speed by an order of magnitude while preserving spectral integrity. By integrating high-speed Raman imaging with supervised machine learning, we aimed to define unique biochemical fingerprints specific to cell type. We hypothesized that intrinsic biochemical composition alone is sufficient to distinguish cellular identity and tumor subtype. To test this, we generated metabolic maps of diverse brain-derived cell types--including astrocytoma, oligodendroglioma, and glioblastoma cells--using coherent Raman spectroscopy at single-cell resolution. Patient-derived brain tumor cell lines representing genetically heterogeneous backgrounds were analyzed. Samples were stratified by IDH1 mutation status (IDH1-mutant and IDH1-wild-type) and histologically classified as oligodendroglioma or astrocytoma. Raman spectral data were acquired from 286 live single cells across the two principal molecular classes, with further subdivision into two histologic subtypes within the IDH1-mutant group. Classification was performed using an XGBoost model with shallow tree depth (1-3), a 20% held-out test set, and grouped, stratified 5-fold cross-validation to control for sample-level bias. The machine learning framework distinguished IDH1-mutant from IDH1-wild-type cells with a ROC-AUC of 0.78 and further discriminated IDH1-mutant astrocytoma from oligodendroglioma cells with a ROC-AUC of 0.81. Feature importance analysis demonstrated that separation between IDH1-mutant and IDH1-wild-type cells was driven primarily by Raman peaks associated with protein amide bands, total NADH, unsaturated fatty acids, and heme-related vibrational modes. Within the IDH1-mutant class, discrimination between oligodendroglioma and astrocytoma was driven by lipid-rich vesicle signatures, protein/polyamide amide bands, and lipid-associated spectral features. Together, these findings support the feasibility of label-free, machine learning-assisted Raman profiling to resolve clinically relevant glioma subtypes at single-cell resolution. This scalable analytical framework provides a translational platform for investigating metabolic heterogeneity, therapeutic response, co-culture systems, and patient-derived organoid models.

cancer biology↗

bioRxiv2026

Network-based analysis of glioblastoma identifies patient communities and cluster-specific biomarkers

Glioblastoma is an aggressive and highly heterogeneous brain tumor with poor prognosis despite multimodal treatment strategies. Understanding the molecular diversity of the disease is essential for improving tumor stratification and identifying potential therapeutic targets. In this study, we investigate whether network-based analysis can reveal biologically meaningful subgroups of glioblastoma tumors. Using RNA sequencing and mutation data from the TCGA-GBM cohort, we constructed patient-specific protein-protein interaction networks based on genes that are differentially expressed or harbor somatic mutations. These networks capture the molecular alterations associated with individual tumors within the context of the human interactome. We then derived similarities between tumors using a binary representation of network nodes and the Jaccard similarity metric, enabling the construction of a patient similarity graph. Community detection algorithms (Louvain and Leiden) were applied to this graph to identify clusters of tumors with similar molecular network profiles. Our analysis revealed six tumor communities characterized by distinct gene compositions and enriched biological processes. For each community, we identified candidate biomarkers and network hubs that may represent potential therapeutic targets. Several of the identified genes correspond to known drug targets, while others represent potential candidates for further investigation. These results illustrate how integrating molecular alterations with network-based modeling can help stratify glioblastoma tumors and uncover molecular mechanisms that may guide the development of more personalized therapeutic strategies.

bioinformatics↗

bioRxiv2024

Kupffer cell and recruited macrophage heterogeneity orchestrate granuloma maturation and hepatic immunity in visceral leishmaniasis

In murine models of visceral leishmaniasis (VL), parasitization of resident Kupffer cells (resKCs) is responsible for early growth of Leishmania infantum in the liver, which leads to granuloma formation and eventual parasite control. We employed the chronic VL model, and revealed an open niche established by KCs death and their migration outside of the sinusoids, resulting in their gradual replacement by monocyte-derived KCs (moKCs). While early granulomas were composed of resKCs, late granulomas were found outside of the sinusoids and contained resKC-derived macrophages, and monocyte-derived macrophages (momacs). ResKCs and moKCs within the sinusoids were identified as homeostatic/regulatory cells, while resKC-derived macrophages and momacs within late granulomas were pro-inflammatory. Despite the infection being largely confined to the resKC-derived macrophages, in the absence of monocyte recruitment, parasite control was strongly compromised. Macrophage heterogeneity, involving migration and reprogramming of resKCs, along with recruitment of monocyte-derived cells, is a hallmark of granuloma maturation and hepatic immunity in VL.

immunology↗

bioRxiv2024

Rapid detection, quantification and speciation of Leishmania using real-time PCR and DNA sequencing at the rRNA Internal Transcribed Spacer 2

The ability to discriminate infection between closely related Leishmania species within the Viannia species complex, specifically L. braziliensis, L. guyanensis and L. panamensis is critical to inform the clinical diagnosis and determine the most efficacious treatment modality. We designed a nested primer set targeting the rRNA Internal Transcribed Spacer 2 (ITS2), located on Chromosome 27, to distinguish among all human infective Leishmania species. Separate nested and single primer pairs were developed for conventional and quantitative PCR approaches respectively. Species-specific single nucleotide polymorphisms and indels located within the PCR products were identified by Sanger sequencing. This single locus approach provides a sensitive and specific platform to identify the species of Leishmania causing infection.

microbiology↗

bioRxiv2021

Stress conditions promote the mating competency of Leishmania promastigotes in vitro marked by expression of the ancestral gamete fusogen HAP2

Leishmania are protozoan parasites transmitted by the bite of sand fly vectors producing a wide spectrum of diseases in their mammalian hosts. These diverse clinical outcomes are directly associated with parasite strain and species diversity. Although Leishmania reproduction is mainly clonal, a cryptic sexual cycle capable of producing hybrid genotypes has been inferred from population genetic studies, and directly demonstrated by laboratory crosses. Experimentally, mating competency has been largely confined to promastigotes developing in the sand fly midgut. The ability to hybridize culture promastigotes in vitro has been limited so far to low efficiency mating between two L. tropica strains, L747 and MA37, that mate with high efficiency in flies. Here, we show that exposure of promastigote cultures to DNA damage stress produces a remarkably enhanced efficiency of in vitro hybridization of the L. tropica strains, and extends to other species, including L. donovani, L. infantum, and L. braziliensis, a capacity to generate intra- and interspecific hybrids. Whole genome sequencing and total DNA content analyses indicate that the hybrids are in each case full genome, polyploid hybrids. Single-cell RNA sequencing of the L747 and MA37 parental lines highlights the transcriptome heterogeneity of culture promastigotes and reveals discrete clusters that emerge post-irradiation in which genes potentially involved in genetic exchange are expressed, including the ancestral gamete fusogen HAP2. By generating reporter constructs for HAP2, we could select for mating-competent and mating-incompetent promastigotes. Overall, this work reveals that there are specific populations involved in Leishmania mating associated with a discernible transcriptomic signature, and that stress facilitated in vitro hybridization can be a transformative approach to generate large numbers of hybrid genotypes between diverse species and strains.

microbiology↗