Biology subjects

Gupta, S.

Publications and source records attributed to Gupta, S..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

A fusion Cell-Permeable C16orf74 Peptide Selectively Disrupts Calcineurin-NFAT Interaction and Inhibits T-cell Activation Without Cytotoxicity

Calcineurin (Cn) is a protein phosphatase that initiates T-cell activation by dephosphorylating the transcription factor NFAT, driving its nuclear translocation and the transcription of immune-related genes. While clinical immunosuppressants like Cyclosporine A (CsA) potently inhibit Cn, they completely block its catalytic site, leading to non-specific inhibition and severe off-target toxicity. Selectively targeting the specific protein-protein interaction (PPI) between Cn and NFAT presents a safer therapeutic strategy. We previously identified the C16orf74 (C16) peptide as a high-affinity Cn-NFAT PPI inhibitor; however, its utility in cellular systems is restricted by poor membrane permeability. In this study, we evaluated cell-penetrating peptide (CPP) conjugates of C16 with an N-terminus transactivator of transcription (TAT) and polyarginine (R11) to enable efficient intracellular delivery. Structural modeling, fluorescence polarization displacement, and pull-down assays confirmed that the CPP-C16 conjugates retain the ability to compete with an NFAT-derived peptide and bind Cn. Fluorescence microscopy demonstrated efficient intracellular entry of TAT-C16 and R11-C16 in mammalian cells, and effective inhibition of NFAT nuclear translocation and attenuation of downstream NFAT-dependent transcriptional activity of the IL-2 gene in human T cells at concentrations of 10 {micro}M or lower. Crucially, unlike CsA, the CPP-C16 peptides exhibited minimal cytotoxicity even at high concentrations of up to 50 {micro}M, establishing a potential safe therapeutic window. These findings establish CPP-C16 conjugates as effective, cell-permeable, and non-toxic inhibitors of the Cn-NFAT signaling axis, providing the basis for the development of PPI-directed immunosuppressants.

bioengineering↗

bioRxiv2026

Febuxostat enhances the anti-tumor efficacy of 2-fluoroadenine and 5-methylthioadenosine in MTAP-deleted cancer

BackgroundHomozygous deletion of the methylthioadenosine phosphorylase (MTAP) gene is a frequent genetic alteration in cancer. MTAP, which creates adenine from 5-methylthioadenosine (MTA), is constitutively expressed in all tissues throughout the body. Previously, we described a novel strategy to specifically target MTAP-deleted cancer cells by combining the antipurine prodrug 2-fluoroadenine (2FA) with MTA. In vitro, this combination efficiently killed MTAP- cancer cells, but in vivo the combination was much less effective in vivo. Here, we explored the role of xanthine oxidase (XO) in this process. Materials and MethodsVarious combinations of 2FA, MTA, and the xanthine oxidase inhibitor febuxostat (FX) were tested in various cancer cell lines grown in vitro and in mice. LC-MS/MS was used to examine the levels and ratio of intracellular 2-FA-containing nucleotides compared to adenine-containing nucleotides. Results and conclusionsThe treatment of cells with 2FA+MTA in vitro resulted in much higher 2FANP/ANP ratios than the same treatment in vivo. The addition of XO to culture media in vitro effectively abolished the killing by 2FA, and this effect was fully reversed by the addition of febuxostat (FX), a xanthine oxidase inhibitor. In vivo, the addition of FX to 2FA results in increased cell killing and toxicity and a 1000% increase in the amount of 2FA converted to 2-FA-monophosphate (2FAMP). Xenograft studies using MTAP- HT1080 and MiaPaCa-2 cell lines have shown that a 2FA/MTA/FX cocktail can cause tumor regression in vivo. These studies suggest that the combination of 2FA/MTA/FX should be explored as a treatment for MTAP- cancer.

cancer biology↗

bioRxiv2026

AEGIS reveals epitope- and clone-resolved convergence of CNS B and T cell autoreactivity in ROHHAD

Autoimmune diseases arise when B and T lymphocytes lose tolerance to self. Yet in most disorders, the underlying molecular determinants, including autoantibodies, epitopes and lymphocyte clones that drive tissue injury remain undefined. Rapid-onset obesity with hypothalamic dysfunction, hypoventilation and autonomic dysregulation (ROHHAD) is a rare and often fatal pediatric neuroendocrine syndrome with strong evidence of antigen-driven paraneoplastic autoimmunity, including association with the intracellular autoantigen ZSCAN1. However, the effector immune circuit and the epitope-level determinants operating within the hypothalamus and brainstem have remained unknown. To address this challenge in ROHHAD and more broadly in autoimmune disease, we developed the Autoimmune Epitope and immunoGlobulin/Immune-receptor identification System (AEGIS), an integrated framework that links immune repertoires to their cognate self-epitopes. AEGIS combines B cell and T cell receptor profiling from sites of tissue injury with high-resolution epitope mapping, direct sequencing of antigen-specific autoantibodies, in silico antibody-antigen folding, selection, and T cell antigen discovery. Applied to a deeply phenotyped child with ROHHAD, AEGIS revealed a compartmentalized, clonally restricted immune response in which brain-deposited IgG and expanded cerebrospinal fluid B cell and CD4 T cell clonotypes converged on shared ZSCAN1 epitopes, resolved to minimal determinants and peptide-MHC ligands. These findings provide a clone- and epitope-linked mechanistic map of ROHHAD autoimmunity and establish a generalizable framework for identifying candidate pathogenic clones and antigens across diverse autoimmune diseases.

immunology↗

bioRxiv2026

The abscopal effect of Combination IRE with anti-PD-1 achieves local ablation and systemic control of PDAC

Irreversible electroporation (IRE) has shown promise for treating pancreatic ductal adenocarcinoma (PDAC), but whether IRE can induce an abscopal effect is not established. We demonstrated that the combination of IRE and anti-PD-1 antibody could trigger robust abscopal effects in preclinical models of metastatic PDAC. Data from multiple in vivo models, RNA-seq, scRNA-seq, and spatial immunofluorescence provide compelling evidence that IRE induced mitochondrial dysfunction and cellular stress, which triggered activation of the cGAS-STING pathway and subsequent systemic antitumor effects. IRE also led to inflammatory response characterized by tumor infiltration of myeloid cells and their polarization toward M1 state, turning immunologically "cold" tumors into "hot" tumors. Moreover, the presence of T cell/B cell clusters in tumors from mice treated with IRE plus PD-1 and the lack of antitumor efficacy in B cell knockout mice bearing orthotopic murine PDAC tumors indicate that B cells play an important role in IRE-mediated systemic antitumor immunity. SignificanceThis study shows that IRE plus a checkpoint inhibitor represents a promising therapeutic strategy for PDAC and supports advancing this treatment toward clinical translation. Our data also support potential combination strategies with immunomodulatory agents that can recruit and reprogram B cells to support T cell activation and cytotoxic effector functions.

immunology↗

bioRxiv2026

Tracking vaginal microbiome transitions in bacterial vaginosis for cues of antibiotic resilience

BackgroundBacterial vaginosis (BV) is a common and difficult-to-treat vaginal disorder, with significant implications for reproductive health, particularly in low and middle-income countries. Clinical cure based on symptom resolution or Nugent scores often do not correspond to restoration of healthy vaginal microbiome. Factors underlying treatment failure remain poorly defined, warranting the need for understanding post-treatment microbiome dynamics to improve long-term outcomes. ObjectivesTo delineate longitudinal vaginal bacteriome dynamics, integrating microbial composition, transition patterns, and clinical symptoms in a closely followed cohort of women with BV based on treatment outcome. MethodsVaginal swabs from reproductive-age women (18-45 years) were collected and classified as BV-positive ([≥]7) or healthy ([≤]3) using Nugent scoring. BV cases were treated using single-dose secnidazole and followed for three months. Sociodemographic, clinical, and behavioral data were statistically analyzed across groups. Vaginal microbiome composition was assessed using 16S rRNA sequencing, evaluating taxonomic profiles, alpha and beta diversity, differential abundance, and co-occurrence networks. ResultsAntibiotic treatment reduced overall microbial diversity and shifted community composition toward healthy controls, though relapse samples retained higher diversity of BV-associated taxa such as Sneathia, Dialister, and Gardnerella, while no-relapse and control groups showed higher Lactobacillus abundance. Corynebacterium amycolatum appeared protective, while Mycoplasma and Fusobacterium played symptom-specific roles. Microbial network analysis showed denser and more persistent associations in baseline and relapse groups, with Sneathia remaining a central node. ConclusionShort-term symptom resolution in BV does not correspond to full microbial recovery; necessary for long-term remission. Functional traits of resilient taxa like Sneathia, Fannyhessea and Dialister may confer resilience and enable recolonization, undermining long term treatment efficacy.

microbiology↗

bioRxiv2026

A multitask single-cell analysis framework with knowledge graph as a prior

Single-cell RNA sequencing has reshaped cancer biology by revealing the cellular and molecular diversity of the tumor microenvironment, yet most analysis pipelines still treat clustering, annotation, pathway scoring, metabolic inference, and cell-cell communication as separate tasks. Here, we present scKNIFE (Knowledge graph-based NMF for Inference of Functional Entities), a unified graph-regularized non-negative matrix factorization framework that jointly reconstructs single-cell expression profiles and infers activities of diverse biological entities, including pathways, cell-type markers, metabolic reactions and tasks, ligand-receptor interactions, and cell-state programs. By embedding a heterogeneous knowledge graph directly into the factorization objective, scKNIFE propagates information between biologically related entities through Laplacian regularization while sparsity constraints preserve interpretability. The integrated prior graph spans 47,274 nodes and 506,620 edges, assembled from complementary resources including Gene Ontology, Reactome, Hallmark gene sets, Human-GEM, CellChat, PanglaoDB, Cytopus, and Tabula Muris-derived annotations. Across multiple cancer-focused single-cell datasets, scKNIFE yields competitive to leading performance for clustering and cell-type annotation, while also recovering biologically coherent metabolic programs that agree with dedicated metabolism-focused methods and capture therapy-response-associated states. In addition, the framework supports downstream inference of cell-type-specific biological activities from a single latent representation. Together, these results establish scKNIFE as a modular and extensible framework for end-to-end biological interpretation of single-cell cancer transcriptomics

cancer biology↗

bioRxiv2026

GANGE: Achieving Sequencing Without Sequencing With Diffusion Guided Generative Genomic Transformer

The genome of a species is its book of life, but opening that book remains a costly affair due to the limitations the existing sequencing technologies pose. Short reads sequencers struggle to capture long and complex genomes, though have high fidelity rate. To counter that long reads from IIIrd generation sequencers are used, which are full of indel errors. Thus, reads from both approaches are collectively used with very high coverage, making the sequencing projects unreasonably high of cost and unapproachable to majority. Here we present a first of its kind generative deep-learning system, GANGE, which not just recovers the correct sequence with high accuracy from indel prone ONT reads at manifold lesser coverage but also extends it by 4kb, achieving sequencing without sequencing, horizontally as well as vertically while maintaining >92% accuracy consistently. This all makes it possible to drastically pull down sequencing project cost. GANGE was tested across A. thaliana, O. sativa genomes and Human chromosome 1 where it delivered outstanding assembly performance. Besides this, it was also used to accurately generate 2kb upstream promoters of all the genes from 12 different species, demonstrating that one can now also take up regulomics research just using RNA data alone when genome sequence is not available. With this all, GANGE brings a democratic turning point in the area of genomics and sequencing research.

bioinformatics↗

bioRxiv2026

ProMaya: a hierarchical universal Deep Learning framework for accurate and interpretable Protein-Protein interaction identification

Protein-protein interactions (PPIs) are molecular lego which define the physical states of cells. Accurately identifying PPIs remains challenging due to the interplay of several factors ranging from electrostatic to molecular geometry, topology, and physics. Existing computational approaches capture only fragments of this orchestra, limiting their generalizability across protein families and interaction types. Here, we present ProMaya, a hierarchical multi-scale Graph-transformer framework that integrates 3D atomic geometry, electronic distribution, residue-level structure and disorder, surface mass-density signatures, and large protein language-model embeddings of interacting proteins. Highly comprehensively benchmarked across nine species and 47 GB experimentally validated data, ProMaya achieved consistently >95% average accuracy, outperforming state-of-the-art tools by >12%. As driven by its explainability, the first time introduced atomic and protein language information dramatically boosted it to an outstanding level for PPI discovery in any species, potent to even bypass costly experiments. ProMaya system is freely accessible at https://scbb.ihbt.res.in/ProMaya/

bioinformatics↗

bioRxiv2026

Characterisation of novel Campylobacter jejuni Type VI secretion system (T6SS) effectors and exploration of the roles of the C. jejuni T6SS in bacterial antagonism and human host cell interaction

Campylobacter jejuni is a leading global cause of acute foodborne gastroenteritis however, C. jejuni lacks some of the classic virulence determinants associated with other common enteric bacterial pathogens. In recent years an increasing number of C. jejuni isolates have been identified to encode Type Six Secretion System (T6SS), an apparatus utilised by Gram-negative bacteria to secrete toxic bacterial effectors into neighbouring cells. Despite the prevalence of the T6SS and previous investigations, the roles of the C. jejuni T6SS are still not well characterised especially when compared to our knowledge of other clinically relevant T6SS-positive bacterial species. Additionally, as of yet, no C. jejuni T6SS cargo effectors have been characterised. In this study, we show the C. jejuni 488 strain T6SS displays contact-dependent antagonistic behaviour towards T6SS-negative C. jejuni, Campylobacter coli, Escherichia coli and Enterococcus faecium strains suggesting the presence of the T6SS contributes to the competitive capacity of this C. jejuni T6SS-positive strain. Moreover, this antagonistic activity is linked to the functionality of CJ488_0980 and CJ488_0982, two novel putative Tox-REase-7 domain-containing effectors, which were identified through bioinformatical analysis of the C. jejuni 488 strain genome. Additionally, our investigations propose the C. jejuni 488 T6SS contributes to interaction, invasion and intracellular survival in human intestinal epithelial cells (IEC). Collectively, these initial findings are the first examples of in vitro investigation of putative cargo effectors in Campylobacter spp. and provide valuable insights into the roles of C. jejuni T6SS effectors in bacterial competition and pathogenesis. This study highlights the importance of T6SS as an emerging virulence determinant in Campylobacter spp. warranting further investigation.

microbiology↗

bioRxiv2026

Cross-Modal Training Using Xenium Spatial Transcriptomics Enables DINO-DETR Based Detection of Vascular Niches in H&E Whole-Slide Images

BackgroundTumor vasculature is a key driver of glioma progression, yet routine quantification depends on subjective histopathologic assessment or resource-intensive ancillary immunohistochemistry. A scalable, objective method for vascular phenotyping from routine histology remains an unmet need. MethodsWe leveraged 10x Genomics Xenium spatial transcriptomics data from a glioblastoma specimen to generate molecularly resolved annotations of GBM-associated endothelial cells and pericytes across 809,041 cells. These annotations were transferred to matched H&E-stained sections to train a DINO-DETR-based object detection model using a binary classification scheme (vascular vs. other). The model was validated on four independent Xenium patient slides and applied to a retrospective cohort of 119 diffuse gliomas spanning WHO grades 2-4 (oligodendroglioma, astrocytoma, and glioblastoma) with linked survival data. ResultsBinary vascular cell detection achieved a precision of 0.78, a recall of 0.63, and an F1 score of 0.70, with an overall accuracy of 98.6%. Orthogonal spatial validation confirmed that predicted vascular cells were preferentially localized within annotated blood vessel regions. In subtype-stratified survival analysis, high AI-derived vascular cell proportion was significantly associated with worse overall survival in astrocytoma patients (log-rank p < 0.019). ConclusionCross-modal AI training using spatial transcriptomics enables scalable, molecularly informed vascular quantification directly from routine H&E slides. Within the astrocytoma subtype, where tumor grade is most heterogeneous and vascular phenotype most variable, objective vascular quantification provides independent prognostic information demonstrating the potential of spatially supervised deep learning to extract clinically meaningful microenvironmental signals from universally available histologic material.

pathology↗

bioRxiv2026

Mapping Glycan Binding Profiles of the Gut Bacteria using Liquid Glycan Array (LiGA)

Glycan-microbe interactions are central to gut colonization and host-microbiota communication. Here, we apply a DNA-encoded Liquid Glycan Array (LiGA) to quantify interactions between live gut bacteria and multivalent natural or mirror glycans. LiGA comprises glycosylated M13 bacteriophage bearing silent DNA barcodes that encode glycan identity and density. Using LiGA, we profiled glycan binding across 16 Limosilactobacillus reuteri strains isolated from murine, porcine, poultry, and human hosts, and then extended the approach to profile glycan binding of taxonomically diverse bacteria from three phyla Bacillota, Bacteroidota, Pseudomonadota consisting of nine different species. Recent discussion of mirror-image microorganisms raise a question whether these microorganisms could interact with present-day life by engaging naturally chiral glycans. We demonstrated that this question can be assessed by testing the binding of mirror-image glycans to natural bacteria. Evaluation of enantiomers of common glycan revealed cross-chiral recognition by Escherichia coli and L. reuteri, indicating that these bacteria can used mirror-image glycans to engage for adhesion and potential colonization. By symmetry the same arguments extends to mirror microorganisms and glycans of naturally chirality. We established that LiGA enables efficient characterization of bacterial glycan binding and provides new insights into intestinal microbial ecology.

biochemistry↗

bioRxiv2026

ISG15-USP18 signaling restrains viperin-dependent metabolic antiviral restriction

Type I interferon (IFN-I) responses are tightly regulated to balance antiviral defense with cellular homeostasis. In humans, interferon-stimulated gene 15 (ISG15) functions as a critical negative regulator of IFN-I signaling by stabilizing the IFN negative regulator USP18, yet the functional consequences of ISG15 deficiency remain elusive. Here, we show that the loss of ISG15 exaggerates the JAK-STAT activation and, downstream, amplifies multiple ISGs including the nucleotide-modifying enzyme RSAD2 (viperin). Our quantitative proteomics, genetic reconstitution, and signaling analyses establish that defective USP18 stabilization skews the IFN response towards viperin expression. This amplified ISG network promotes viperin-catalyzed accumulation of the antiviral nucleotide analog ddhCTP, resulting in enhanced inhibition of viral RNA synthesis and the replication of Crimean-Congo hemorrhagic fever virus and SARS-CoV-2. Together, these findings demonstrate an ISG15-USP18-viperin axis that can be targeted to boost the metabolic antiviral restriction.

microbiology↗

bioRxiv2026

TSC2 acting as a transcription factor for miR-514b-3p and regulating PI3K-AKT-MTOR pathway via nucleus

The PI3K-AKT-MTOR signalling pathway is pivotal in regulating cell survival, proliferation, and growth. TSC2 or tuberin acts as a key negative regulator of this pathway by forming a cytoplasmic complex with TSC1 and TBC1D7. While the cytoplasmic role of TSC2 is well established, emerging evidence suggests its nuclear functions. Previously, we have identified TSC2 as a transcriptional repressor of the EREG gene. Building on this foundation, the present study has investigated the transcriptional role of TSC2 in miRNA gene regulation. A genome-wide miRNA microarray profiling of TSC2-depleted SCC131 cells identified 19 upregulated and 24 downregulated miRNAs. Of these, miR-514b-3p emerged as one of the most upregulated miRNAs. The RT-qPCR analysis showed that TSC2 knockdown results in a robust miR-514b-3p upregulation, whereas TSC2 overexpression suppresses its expression in SCC131 cells. Moreover, TSC2 negatively regulates the MIR514B promoter activity in an NLS-dependent manner. Our ChIP analysis confirmed the direct binding of TSC2 to the MIR514B promoter, establishing miR-514b-3p as a transcriptional target of TSC2. We further identified TSPAN9 as a direct downstream target of miR-514b-3p. TSC2 positively regulates TSPAN9 levels by repressing miR-514b-3p, thereby establishing a novel TSC2/miR-514b-3p/TSPAN9 regulatory axis. We further uncovered a crosstalk between TSC2/miR-514b-3p/TSPAN9 axis and the canonical PI3K-AKT-MTOR signalling pathway, where miR-514b-3p positively and TSPAN9 negatively regulates this pathway. Interestingly, AKT functions as an upstream regulator of this axis by modulating the nuclear localization of TSC2. Collectively, this study provides new insights into the non-canonical, nucleus-dependent transcriptional functions of TSC2, thus expanding its role beyond cytoplasmic signalling regulation and underscoring its significance in the cellular signalling networks. Graphical abstractThe diagrammatic representation of the TSC2/miR-514b-3p/TSPAN9 axis and its interaction with the canonical PI3K-AKT-MTOR pathway. Abbreviations: PI3K, Phosphatidylinositol-3 kinase; TSC1, Tuberous sclerosis complex subunit 1; TSC2, Tuberous sclerosis complex subunit 2; TBC1D7, Tre2-Bub2-CDC16 domain family member 7; RHEB, Ras homolog enriched in brain; GTP, Guanosine tri phosphate; GDP, Guanosine di phosphate; MTORC1, Mechanistic target of rapamycin kinase complex 1; S6K1, p70 ribosomal protein S6 kinase; 4E-BP-1, eIF4E-binding protein 1; and TSPAN9, Tetraspanin 9. This figure was created in BioRender (https://app.biorender.com/).

cancer biology↗

bioRxiv2026

Programmable DNA nanocages to modulate pollen tube growth via active uptake

Delivering biomolecules into pollen tubes that deliver sperm cells for plant fertilization remains technically challenging due to thick cell walls and rapid polarized growth, hindering reproductive engineering. DNA nanotechnology offers a promising alternative over current delivery methods due to their biocompatibility, programmable design, low cytotoxicity, and stimulus-responsive properties, yet their application in plants remains underexplored. Here, we provide the first demonstration of tetrahedral DNA nanostructures (TDNs) as nanocarriers for active, endocytosis-mediated uptake into Arabidopsis pollen tubes, enabling spermidine delivery that shortens pollen tube elongation through actin reorganization and ROS modulation. TDN-treated pollen tubes grew through the Arabidopsis stigma and style, underwent capacitation, and maintained attraction to ovules in a semi-in-vivo assay, preserving reproductive fitness. Furthermore, we demonstrate that functionalization of TDNs with nuclear localization signal peptide significantly enhances nuclear localization. Collectively, these findings establish DNA nanostructures as effective nanocarriers for targeted biomolecule delivery and precise pollen tube modulation, advancing crop reproductive engineering. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/710033v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@a09c01org.highwire.dtl.DTLVardef@623943org.highwire.dtl.DTLVardef@9d954corg.highwire.dtl.DTLVardef@1b4c35b_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

bioengineering↗

bioRxiv2026

PTPN1/2 inhibits alveolar macrophage-mediated control of lung metastasis

Metastasis remains the leading cause of cancer mortality, yet effective therapies are limited. While therapeutic responses are influenced by organ-specific immune microenvironments, strategies to pharmacologically modulate these niches remain poorly defined. Here, using the clinical-stage inhibitor ABBV-CLS-484 (AC484) as a chemical probe, we demonstrate that systemic PTPN1/2 inhibition remodels the pulmonary myeloid landscape, specifically activating alveolar macrophages (AMs) toward a tumoricidal state. Integrated single-cell/spatial transcriptomics and functional assays reveal that AC484 promotes AM accumulation within metastatic lesions, elevates their IFN{gamma} production and responsiveness, and enhances their tumor-killing activity. Depletion of AMs diminishes the anti-metastatic efficacy of AC484. Mechanistically, inhibiting PTPN1/2 by AC484 amplifies IFN{gamma}-STAT1 signaling in AMs, while disrupting this pathway impairs their tumor control capability. These findings delineate a distinct innate immune axis where PTPN1/2 acts as a molecular "brake" on AM activation, suggesting that pharmacologically unleashing tissue-resident macrophages offers a therapeutic strategy to overcome metastatic progression, particularly in microenvironments where adaptive immunity is insufficient. SignificanceAMs are potent anti-metastatic immune effectors functionally constrained by PTPN1/2. Pharmacologically inhibiting PTPN1/2 amplifies their IFN{gamma}-STAT1 signaling and tumoricidal activity, establishing a strategy to reactivate tissue-resident immunity against metastasis.

immunology↗

bioRxiv2026

Single-Cell and Spatial Methods for Multimodal Functional Glycan Profiling in Tissues

Glycans regulate multiple physiological processes, including immune recognition and cancer progression. In disease, altered glycan landscapes are interpreted by human lectins. Functional glycan-lectin interactions are difficult to profile because glycans are not genome-encoded and their changes are poorly captured by existing multimodal methods. We present two platforms, single-cell outlining and transcriptome sequencing (scGOAT-seq) and GlycoScope, which use human lectins to enable functional glycan accessibility into single-cell and spatial multiomic measurements. ScGOAT-seq quantifies lectin-accessible glycan states with gene expression, while GlycoScope enables multiplexed in situ co-detection of glycans and proteins in tissues. Applying these approaches to immune cells, we identify stimulus-specific glycan remodeling and show that distinct Siglec-ligand-defined programs stratify immune activation states not captured by traditional methods; in follicular lymphoma, GlycoScope, resolves spatial glycan programs associated with malignant B cells and localized immune microenvironments. The presented methods provide a general framework for integrating functional glycan accessibility into single-cell and spatial multiomics.

molecular biology↗

bioRxiv2026

Selective Sugar Transport Supports Proteus mirabilis Fitness in the Urinary Tract

Proteus mirabilis is a leading cause of complicated urinary tract infections (UTIs). Prior work showed P. mirabilis metabolizes sugars during experimental UTI, yet the role of sugar import systems in pathogenesis remains poorly defined. To investigate this, we generated a panel of 47 targeted mutants in predicted sugar transporter genes and assessed their growth in vitro and fitness in vivo. Growth screening in nutrient-rich and minimal media revealed carbon source-dependent defects in several phosphotransferase system (PTS) mutants, including ptsH and ptsI. Pooled insertion sequencing (In-seq) identified xapB, ptsH, and ptsI as in vivo fitness factors, with validation in a traditional murine co-challenge model. Functional studies showed that xapB, annotated as a xanthosine permease, did not support xanthosine or guanosine uptake in P. mirabilis, suggesting misannotation. Dissection of the PTS network revealed that a triple mutant lacking scrA, ulaC, and ptsG recapitulated the ptsH phenotype in vivo. To evaluate whether increased sugar availability exacerbates these defects, we modeled glucosuria using the SGLT2 inhibitor dapagliflozin in CBA/J mice. Dapagliflozin treatment significantly increased urinary glucose and enhanced P. mirabilis colonization. There was an inverse correlation between colonization and urinary glucose, but only in untreated mice. These findings reveal limitations in genome-based transporter annotation, establish a functional link between sugar import and P. mirabilis fitness during UTI, and demonstrate that host metabolic conditions such as glucosuria can influence the severity of infection. AUTHOR SUMMARYAll living organisms require nutrients to grow, survive, and cause disease. Bacteria like Proteus mirabilis, which causes urinary tract infections, rely on specialized systems to import and metabolize sugars available in the host environment. In this study, we systematically disrupted 47 genes predicted to encode sugar transporters in P. mirabilis and tested their contribution to infection in a mouse model. We identified three key genes (xapB, ptsH, and ptsI) that were critical for colonization. Further analysis showed that many sugar transporters in P. mirabilis were misannotated, and predicted substrates like sucrose and cellobiose were not utilized by the bacterium. We also demonstrated that high sugar conditions, mimicking diabetic urine using the drug dapagliflozin, worsened infection and increased disease severity. These results highlight the importance of carbohydrate acquisition for P. mirabilis during infection and emphasize the need to experimentally validate gene function rather than rely on predictions based on other bacteria like E. coli.

microbiology↗

bioRxiv2026

A Multi-Modal AI/ML-based Framework for Protein Conformation Selection and Prediction in Drug Discovery Applications

The development of pharmaceutical drugs is a time-intensive and costly process, with more than 90% of drug candidates failing during preclinical or clinical testing. A major challenge lies in accurately predicting protein-ligand interactions, especially given that traditional computational methods often rely on a single protein conformation, failing to capture biologically relevant structural variability. To address this, we present an AI/ML-based multi-modal framework based on Graph Convolutional Network (GCN) that integrates both global and local protein descriptors to classify binding and non-binding conformations more effectively. Global descriptors capture overarching physico-chemical and structural properties of proteins, while local descriptors--such as pharmacophores--provide site-specific information crucial for modeling ligand interactions. Our GCN based approach demonstrates that integrating local and global structural perspectives significantly improves predictive accuracy and robustness. By enabling more reliable protein conformation classification, this work contributes toward scalable, AI-driven drug discovery--an increasingly critical goal in response to global health challenges.

molecular biology↗