Biology subjects

Lucas, N.

Publications and source records attributed to Lucas, N..

9 recordsLinked to original sources

bioRxiv2025

Truncating RELA variants drive autoinflammation and autoimmunity by impairing the negative feedback control of NF-kB

The NF-{kappa}B signaling pathway coordinates inflammation, cell survival, and proliferation, while restraining excessive cell death to maintain immune homeostasis. Truncating mutations in RELA, encoding the NF-{kappa}B subunit p65, have been linked to autoinflammation and autoimmunity, but the underlying mechanisms remain incompletely defined. We investigated six patients from five unrelated families carrying novel heterozygous truncating RELA variants. Despite reduced p65 expression, patients exhibited a broad spectrum of inflammatory manifestations alongside elevated baseline and stimulus-induced pro-inflammatory cytokines. Functional analyses in patient-derived cells and mutant RELA knock-in models showed that upstream NF-{kappa}B signaling was intact, but induction of inhibitory regulators such as I{kappa}B and A20 was impaired. This defective feedback control shifted immune homeostasis toward amplified inflammatory responses that depended on the residual activity of the remaining functional RELA allele. Single-cell transcriptomics revealed distinct cell type-specific consequences: monocytes displayed constitutive type I interferon and NF-{kappa}B activation, B cells retained partial compensatory signaling, whereas T and NK cells exhibited transcriptional signatures of cell death pathways. Patient fibroblasts and mutant RELA knock-in cells further confirmed enhanced TNF-induced inflammatory gene expression and hypersensitivity to apoptosis and necroptosis. These findings establish RELA haploinsufficiency as a cause of systemic immune dysregulation, and link defective NF-{kappa}B feedback control to unchecked inflammation and inflammatory cell death. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/687461v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1f0bfe6org.highwire.dtl.DTLVardef@c6cb60org.highwire.dtl.DTLVardef@1522accorg.highwire.dtl.DTLVardef@177c724_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Split-luciferin Assay for Real-time Measurement of Cytosolic Drug Accumulation in Live Mycobacteria

Tuberculosis causes over one million deaths annually and remains the leading cause of death from a single infectious agent. The emergence of multidrug-resistant Mycobacterium tuberculosis strains highlights the urgent need for new antibiotics, a pursuit hindered by the bacteriums complex cell envelope. As most anti-tuberculosis agents act on intracellular targets, assessing cytosolic drug accumulation is critical. Conventional approaches generally quantify whole-cell association without resolving subcellular localization. Moreover, no current method permits real-time monitoring of drug accumulation in live mycobacterial cells. Here, we present a split-luciferin-based assay to quantify molecular accumulation in mycobacteria. Using this approach, we quantified the cytosolic accumulation of diverse small-molecule antibiotics and polyarginine peptides conjugated via a disulfide-linked D-cysteine tag. We also show the localization of a polyarginine peptide inside of mycobacteria in infected macrophage cells, demonstrating that these peptides can cross multiple accumulation barriers. Our findings establish the first assay for real-time quantification of cytosolic molecular accumulation in live mycobacteria, addressing a longstanding methodological gap and enabling mechanistic insights into intracellular drug uptake.

microbiology↗

bioRxiv2024

Federated deep learning enables cancer subtyping by proteomics

Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing data from a pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain on the hold-out test set (n=625) in 14 cancer subtyping tasks compared to local models, and matching centralized model performance. The approachs generalizability was demonstrated by retraining the global model with data from two external DIA-MS cohorts (n=55) and eight acquired by tandem mass tag (TMT) proteomics (n=832). ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data, e.g., for discovering predictive biomarkers or treatment targets, while maintaining data privacy. Statement of SignificanceA federated deep learning approach applied to human proteomic data, acquired using two distinct proteomic technologies from 40 tumor cohorts from eight countries, enabled accurate cancer histopathological subtyping while preserving data privacy. This approach will enable privacy-compliant development of large-scale proteomic AI models, including foundation models, across institutions globally.

cancer biology↗

bioRxiv2024

Assessment of a high-throughput mass spectrometry method to accelerate biomarker discovery in clinical cancer cohorts using volumetric absorptive microsampling (VAMS) devices.

Identification of biomarkers of early-stage disease typically requires analysis of very large cohorts which can only be reasonably achieved using high-throughput methods. We have developed and optimised novel methods for whole blood analysis using volumetric absorptive microsampling devices to produce over 3000 protein identifications by LCMS on a Q Exactive HF-X Orbitrap. These methods were tested using a set of whole blood samples from lung cancer patients and matching healthy controls finding 455 differentially expressed proteins, using a mid-throughput method enabling analysis of 18 samples per day. To increase throughput for larger clinical cohorts, a 60-sample per day method was tested on a Sciex ZenoTOF 7600. The high-throughput method produced 1.5-fold fewer protein identifications and a higher overall % CV compared to the mid-throughput method. Despite the lower numbers, it produced a set of 36 disease-relevant and discriminatory differentially expressed proteins that, using a machine learning model, could differentiate between the disease and control samples with an area under the ROC curve (AUC) of 88.9% using random forest algorithms. These data support the use of high-throughput mass spectrometry methods to screen large cohorts for diagnostic biomarkers that can then be followed up with more targeted analyses.

biochemistry↗

bioRxiv2024

Investigating the use of novel blood processing methods to boost the identification of biomarkers of non-small cell lung cancer

1.Background and objectivesDiagnosis of non-small cell lung cancer (NSCLC) currently relies on imaging and in-clinic visits, however these methods are not effective at detecting early-stage disease. The investigation into blood-based biomarkers aims to simplify the diagnostic process and has the potential for identifying disease-associated changes before they can be seen using imaging techniques. Methods and designIn this study, plasma and frozen whole blood cell pellets from patients with NSCLC and healthy controls were processed using both classical as well as novel techniques to produce a unique set of 4 sample types from a single blood draw. Samples were analysed using 12 commercially available immunoassay kits in addition to liquid chromatography mass spectrometry using a Q Exactive HF-X Orbitrap to collectively screen 3974 proteins as potential biomarkers. Results and conclusionsAnalysis of all sample types produced a set of 522 differentially expressed proteins, with conventional blood analysis (proteomic analysis of plasma) accounting for only 7 of that total. Boosted regression tree analysis of the differentially expressed proteins produced a panel of 13 proteins that were able to discriminate between controls and NSCLC patients with an area under the ROC curve (AUC) of 0.864 for the set. Our rapid and reproducible blood preparation and analysis methods enable the production of high-quality data from small aliquots of complex samples that are typically seen as requiring significant fractionation prior to proteomic analysis.

cancer biology↗

bioRxiv2023

Heat n Beat: A universal high-throughput end-to-end proteomics sample processing platform in under an hour

Proteomic analysis by mass spectrometry (MS) of small ([≤]2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a near universal, rapid and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centred on a one tube homogenisation and digestion method called Heat n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided to separate challenges. The initial sample washing, and final peptide clean-up steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin-embedded (FFPE). Thirdly, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenisation treatment, and reduction, alkylation and proteolysis were optimised into a single step. The refinements produced near doubled peptide yield, delivered consistently high digestion efficiency of 85-90%, and required only 38 minutes for core processing in a single tube, with total processing time being 53-63 minutes. The robustness of HnB was demonstrated on six organ types, a cell line and a cancer biopsy. Its suitability for high throughput applications was demonstrated on a set of 1,171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 hours, producing highly consistent peptide yield and quality for over 3,513 MS runs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/559846v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@4b8399org.highwire.dtl.DTLVardef@1acc573org.highwire.dtl.DTLVardef@1d7155eorg.highwire.dtl.DTLVardef@1bbef10_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2022

SAMHD1 controls innate immunity by regulating condensation of immunogenic self RNA

Recognition of pathogen-derived foreign nucleic acids is central to innate immune defense. This requires discrimination between structurally highly similar self and nonself nucleic acids to avoid aberrant inflammatory responses as in the autoinflammatory disorder Aicardi-Goutieres syndrome (AGS). How vast amounts of self RNA are shielded from immune recognition to prevent autoinflammation is not fully understood. Here we show that SAM domain and HD domain-containing protein 1 (SAMHD1), one of the AGS-causing genes, functions as a single-stranded RNA (ssRNA) 3exonuclease, the lack of which causes cellular RNA accumulation. Increased ssRNA in cells leads to dissolution of RNA-protein condensates, which sequester immunogenic double-stranded RNA (dsRNA). Release of sequestered dsRNA from condensates triggers activation of antiviral type I interferon via retinoic acid-inducible gene I-like receptors. Our results establish SAMHD1 as a key regulator of cellular RNA homeostasis and demonstrate that buffering of immunogenic self RNA by condensates regulates innate immune responses.

immunology↗

bioRxiv2022

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

systems biology↗