Biology subjects

Hupp, T.

Publications and source records attributed to Hupp, T..

7 recordsLinked to original sources

bioRxiv2026

Novel native serum peptidomics workflow enables the discovery of circulating subtype-specific peptide biomarkers in acute ischemic and haemorrhagic stroke

Novel serum peptidomics offers a direct insight into proteolytic activity, tissue injury, and systemic signaling. Nevertheless, existing workflows suffer from low peptide yields, low throughput, and limited recovery of low-abundance species. Here we present a native serum peptidomics protocol that integrates mild acid treatment, solid-phase extraction with molecular weight cutoff filtration and data-independent acquisition mass spectrometry (DIA-MS). The protocol requires less than 100 {micro}l of serum or plasma, is completed within hours, time-cost-effective and compatible with 96-well formats without specialized equipment. Applied to a proof-of-concept cohort of patients with acute ischemic stroke (AIS), intracranial haemorrhage (ICH), and healthy controls, the workflow identified over 12,000 peptides, exceeding the three-fold threshold of existing peptidomics approaches. DIA-MS analysis across independent batches demonstrated 78-83% peptide overlap and consistent fold-change directionality. We further introduce peptide locus analysis, which aggregates overlapping peptides within defined protein regions. This approach revealed bidirectional regulation within individual precursor proteins such as the fibrinogen alpha chain (FIBA), resolving intraprotein proteolytic dynamics. Three candidate peptides from TYB4, CO4B, and ITIH4 proteins accurately distinguished stroke subtypes and controls, while characteristic shifts in peptide physicochemical properties were observed across strokes. This workflow substantially advances the sensitivity, throughput, and biological resolution of serum peptidomics for quantitative multi-biomarker discovery, validation and its output promises effective implementation of AI/ML models aiming for new dimensions in diagnostics, prognostics, prediction and monitoring.

neuroscience↗

bioRxiv2025

DoggifAI: a transformer based approach for antibodycaninisation

Antibody translation across species offers a compelling strategy to extend the vast and expensive investments in human therapeutic antibodies to veterinary oncology, with applications in both veterinary medicine and comparative oncology. While precise, low-immunogenic treatments are essential for canine cancer care, traditional species conversion methods rely on ad hoc bioinformatics modifications. These methods often implicitly decouple the framework (FR) and complementarity-determining regions (CDRs), ignoring how structural changes in FRs can affect the conformation and function of CDRs. This can compromise binding specificity and require costly high-throughput in vitro screening. To address this, we present DoggifAI, a transformer model that translates non-canine antibody sequences into canine ones by generating species-appropriate framework regions (FRs) based on desired CDRs. This allows the model to better preserve structural compatibility between FRs and CDRs. The model is pretrained in a T5-style text-to-text denoising task on a large multispecies antibody dataset, which allows further finetuning on a much smaller species-specific dataset. DoggifAI generates highly canine-like antibodies and shows promising results in preserving binding specificity. To support further progress in this field, we also release a curated dataset of over 430,000 unique canine antibody chain sequences, significantly expanding the public sequence repertoire. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/656573v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@13c10adorg.highwire.dtl.DTLVardef@6afca6org.highwire.dtl.DTLVardef@1f11961org.highwire.dtl.DTLVardef@1b9563a_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG HighlightsO_LIWe show that transformer models are capable of generating plausible antibody framework regions based on CDRs C_LIO_LIWe show that resulting framework regions are highly recognisable as coming from the desired species C_LIO_LIWe show promising results for the retention of binding specificity when translating antibody sequences in this way C_LIO_LIWe release a large, high-quality dataset of canine antibody sequences to support future research C_LI

bioinformatics↗

bioRxiv2025

Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN

Promiscuity in T-cell antigen landscapes refers to the dual flexibility of peptides binding multiple MHC alleles and MHC alleles presenting diverse arrays of peptides. By understanding how neoantigens are shared across varied HLA backgrounds, promiscuity analysis can inform the selection of cancer-vaccine targets that reach a wider segment of the population and help refine patient stratification for diverse immunotherapies. We expand the concept of promiscuity to encompass peptides, MHC alleles, individuals, populations, and genomic regions. Our CARMEN database release harmonizes data from 72 publications (2,323 samples) across tissue types, with a focus on cancer. Using Gibbs clustering and dimensionality reduction (UMAP), we systematically map promiscuity and immunological versatility across these biological levels. Gene and mutation analysis reveals recurrent cancer mutations in highly promiscuous genomic regions, highly mutated cancer genes that avoid presented regions, and sheds light on genomic regions important to response to immunotherapy.

bioinformatics↗

bioRxiv2022

SRSF1 as a novel interacting partner for IFITM1/3 unravels the emergent role of IFITM1/3 mediating protein expression

IFITM proteins play a role in cancer cell progression through undefined mechanisms. Here, we propose an emergent role of IFITM1/3 regulating protein synthesis. SBP-tagged IFITM1 protein was used to identify an association of IFITM1 protein with the cytosolic isoform of SRSF1 that transports mRNA to the ribosome. This cytosolic association was confirmed in situ using proximity ligation assays for SRSF1 and IFITM1/3, suggesting a role associated with translation. Accordingly, IFITM1/3 were shown to interact with HLA-B mRNA in response to IFN{gamma} stimulation using RNA-protein proximity ligation assays. In addition, shotgun RNA sequencing in IFITM1/IFITM3 null cells and wt-SiHa cells indicated that reduced HLA-B gene expression does not account for lowered HLA-B protein synthesis in response to IFN{gamma}. Furthermore, ribosome profiling using sucrose gradient sedimentation identified a reduction in 80S ribosomal fraction an IFITM1/IFITM3 null cells compared to their wild-type counterpart, partially reverted by IFITM1/3 complementation. Our data all together link the binding of IFITM1/3 proteins to HLA-B mRNA and SRSF1 as a mechanism to catalyze the synthesis of target proteins, suggesting an RNA chaperonin role for IFITM1/3 proteins. SignificanceIFITMs are widely studied for their role in inhibiting viruses, and multiple studies have associated IFITMs with cancer progression. However, mechanistic insights are not well understood. Our study proposes that IFITMs have a role regulating protein synthesis, a pivotal function highly relevant for viral infection and cancer progression. Our results suggest that IFITM1/3 is present in the ribosomal fraction and regulates particular protein expression; among them, we identified HLA-B. Changes in HLA-B expression could impact the presentation and recognition of oncogenic antigens on the cell surface by cytotoxic T cells and, ultimately, limit tumor cell eradication. In addition, the role of IFITMs mediating protein translation is relevant, as has the potential of regulating the expression of viral and oncogenic proteins.

cancer biology↗

bioRxiv2021

High-content profiling reveals a unified model of copper ionophore dependent cell death in oesophageal adenocarcinoma

Background and AimsOesophageal adenocarcinoma (OAC) is of increasing global concern due to increasing incidence, a lack of effective treatments, and poor prognosis. Therapeutic target discovery and clinical trials have been hindered by the heterogeneity of the disease, lack of driver mutations, and the dominance of large-scale genomic rearrangements. In this work we have characterised three potent and selective hit compounds identified in an innovative high-content phenotypic screening assay. The three hits include two approved drugs; elesclomol and disulfiram, and another small molecule compound, ammonium pyrrolidinedithiocarbamate. We uncover their mechanism of action, discover a targetable vulnerability, and gain insight into drug sensitivity for biomarker-based clinical trials in OAC. MethodsElesclomol, disulfiram, and ammonium pyrrolidinedithiocarbamate were systematically characterised across panels of oesophageal cell lines and patient-derived organoids. Drug treated oesophageal cell lines were morphologically profiled using a high-content, imaging platform. Compounds were assessed for efficacy across patient-derived organoids. Metabolomics and transcriptomics were assessed for the identification of oesophageal-cancer specific drug mechanisms and patient stratification hypotheses. ResultsHigh-content profiling revealed that all three compounds were highly selective for OAC over tissue-matched controls. Comparison of gene expression and morphological signatures unveiled a unified mechanism of action involving the accumulation of copper selectively in cancer cells, leading to dysregulation of proteostasis and cancer cell death. Basal omic analyses revealed proteasome and metabolic markers of drug sensitivity, forming the basis for biomarker-based clinical trials in OAC. ConclusionsIntegrated analysis of high-content imaging, transcriptomic and metabolomic data has revealed a new therapeutic mechanism for the treatment of OAC and represents an alternative target-agnostic drug discovery strategy.

cancer biology↗

bioRxiv2020

Reflux of Endoplasmic Reticulum proteins to the cytosol yields inactivation of tumor suppressors

In the past decades many studies reported Endoplasmic Reticulum (ER) resident proteins to localize to the cytosol but the mechanisms by which this occurs and whether these proteins exert cytosolic functions remain unknown. We found that select ER luminal proteins accumulate in the cytosol of glioblastoma cells isolated from mouse and human tumors. In cultured cells ER protein reflux to the cytosol occurs upon proteostasis perturbation. As such we investigated whether refluxed proteins gain new functions in the cytosol thus providing advantage to tumor cells. Using the ER luminal protein AGR2 as a model, we showed that it is refluxed to the cytosol where it binds and inhibits the tumor suppressor p53. We named this phenomenon ER to Cytosol Signaling (ERCYS) as an ER surveillance mechanism conserved in Eukaryotes to relieve the ER from its contents upon stress and to provide selective advantage to tumor cells through gain-of-cytosolic functions.

cell biology↗

bioRxiv2020

High Content Phenotypic Profiling in Oesophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery

Oesophageal adenocarcinoma (OAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies contributing to poor outcomes for patients diagnosed with OAC. We describe the development and application of a phenotypic-led OAC drug discovery platform incorporating image-based, high-content cell profiling and associated image-informatics tools to classify drug mechanism-of-action (MoA). We applied a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six OAC cell lines representing the genetic heterogeneity of disease, a pre-neoplastic Barretts oesophagus line and a non-transformed squamous oesophageal line. We built an automated phenotypic screening and high-content image analysis pipeline to identify compounds that selectively modified the phenotype of OAC cell lines. We further trained a machine-learning model to predict the MoA of OAC selective compounds using phenotypic fingerprints from a library of reference compounds. We identified a number of phenotypic clusters enriched with similar pharmacological classes e.g. Methotrexate and three other antimetabolites which are highly selective for OAC cell lines. We further identify a small number of hits from our diverse chemical library which show potent and selective activity for OAC cell lines and which do not cluster with the reference library of known MoA, indicating they may be selectively targeting novel oesophageal cancer biology. Our results demonstrate that our OAC phenotypic screening platform can identify existing pharmacological classes and novel compounds with selective activity for OAC cell phenotypes.

cancer biology↗