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Robinson, P. J.

Publications and source records attributed to Robinson, P. J..

12 recordsLinked to original sources

A cotranslational role for selenocysteine in productive protein folding

Selenocysteine (Sec) is generally regarded as the catalytic determinant that distinguishes selenoproteins from their cysteine (Cys)-containing counterparts. Consequently, the biological significance of Sec has largely been attributed to its unique chemical properties within mature proteins. Here, we uncover an unexpected role for Sec in protein biogenesis. Using the endoplasmic reticulum selenoprotein SELENOM as a model, we show that replacement of Sec with Cys redirects the protein into an alternative assembly pathway, resulting in formation of a stable domain-swapped homodimer. Biochemical and structural analyses demonstrate that this phenotype cannot be explained solely by altered active-site chemistry, as dimerisation persists following extensive active-site mutagenesis. Remarkably, reintroduction of Sec at alternative positions suppresses dimer formation, including when Sec is placed within a cleaved signal peptide and is absent from the mature protein. Thus, identical protein sequences can adopt distinct folding outcomes depending on whether Sec incorporation occurred during biosynthesis. These findings reveal a previously unrecognised role for Sec in productive protein folding and raise the possibility that evolutionary retention of the Sec incorporation pathway reflects advantages not only in catalysis but also in protein folding.

cell biology↗

An organ-resolved rat FFPE phosphoproteome map enables directional kinase activity inference

Formalin-fixed paraffin-embedded (FFPE) tissue is the dominant clinical pathology resource yet whether it faithfully preserves organ signalling biology and supports directional regulatory analysis remains unquantified. We generated a phosphoproteome map from eight healthy rat organs, separating preservation effects from biological variation. Using mass spectrometry, we quantified 54,710 phosphosites on 5,994 proteins across receptors, kinase cascades and nuclear regulators. Organ-specific phosphosite signatures matched known physiological and proliferative states. Paired antagonistic phosphosites converted into "activating-minus-inhibitory" indices that quantified net tissue-specific pathway activity, while a "kinase-by-organ activity" matrix resolved functional hierarchies. Joint analysis with an external fresh-frozen phosphoproteome dataset yielded 58,631 phosphosites total, recovering 86% of the 28,888 sites detected in the frozen dataset. Organ identity explained over 92% of the total variance after batch correction, versus under 0.5% for preservation method. Per-organ phosphosite intensities agreed closely between preservation modes except in brain. This establishes that archived pathology tissue supports biologically faithful phosphoproteome analysis at organ, pathway, and site resolution, providing a framework for retrospective signalling studies in clinical archives. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/741173v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@191c1a2org.highwire.dtl.DTLVardef@3f8d21org.highwire.dtl.DTLVardef@4a8525org.highwire.dtl.DTLVardef@6b4b91_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

Patient-Derived Air-Liquid Interface Forebrain Organoids Reveal Functional Synaptic Deficits in Schizophrenia

Schizophrenia (SCZ) is a severe and debilitating neurodevelopmental disorder with lifelong impact on everyday life. Disruptions in synapse functions play a key role in its complex and poorly understood etiological and pathological mechanisms. Here, we investigated both the molecular composition and the spontaneous and stimulated functional properties of synapses in neural organoids from SCZ individuals. Air-liquid interface forebrain organoids (ALI-FOs) were generated from induced pluripotent stem cells (iPSCs) derived from three individuals with SCZ and three healthy controls. At day 170 synaptosomes were enriched and analyzed by data-independent acquisition mass spectrometry to profile the proteome, alongside with TMT-labeled phosphoproteomics both before and after acute KCl-induced depolarization. In parallel, we characterized the PTMome of the surrounding cellular environment, comprising phosphorylation, peptides with free and reversibly modified cysteines, and sialylated N-linked glycopeptides. Functional glutamatergic and GABAergic activity was assessed using calcium imaging to capture spontaneous neuronal signaling. Both conditions exhibited mature synaptic structures, while growth cones were observed only in SCZ-derived ALI-FOs, indicative of ongoing or delayed synaptogenesis. Proteomic analysis of synaptosome preparations revealed 358 differentially regulated proteins between SCZ and controls and 125 phophoproteins with altered phosphorylation, which clustered into three major categories: (1) synaptogenesis and synapse signaling; (2) cytoskeleton and cell junctions; (3) growth cone dynamics and neurite outgrowth. Analysis of the PTMs in the surrounding cellular environment revealed regulation of key regulatory mechanisms in 526 proteins, supporting the synaptic alterations observed. Notably, components of the Wnt signaling pathway were consistently dysregulated across both the synaptosome preparation and the PTMome in SCZ-derived ALIFOs as compared to controls. Depolarization-induced phospho-signaling revealed SCZ-specific response enriched in synaptic vesicle trafficking pathways. Together, these findings provide new insights into early synaptic alterations in SCZ, highlighting changes not only in protein composition, but more in protein regulatory mechanisms underlying synaptic signaling.

neuroscience↗

Protein-state dysregulation and sex-specific neurodevelopmental signatures in schizophrenia forebrain organoids

Schizophrenia is highly heritable, yet the molecular mechanisms linking genetic risk to abnormal human brain development remain poorly understood. To address this, we generated dorsal forebrain organoids from 17 individuals with idiopathic schizophrenia and 17 age- and sex-matched controls and profiled them across multiple molecular layers, including single-nucleus transcriptomics, quantitative proteomics, metabolomics and deep post-translational modification (PTM) analysis. The organoids reproducibly modelled early cortical development and showed largely similar cellular composition between schizophrenia and control groups. Surprisingly, transcriptomic differences were relatively limited, with the strongest cell-type-specific changes observed in Cajal-Retzius neurons. In contrast, proteomic and particularly PTM-level analyses revealed widespread molecular disruption affecting pathways involved in neuronal migration, neurite development, synaptic function, protein kinase signalling, extracellular matrix organisation and lipid metabolism. Many of the earliest disease-associated changes emerged at the level of protein phosphorylation, consistent with altered neuronal maturation and neurite dynamics. At later developmental stages, schizophrenia organoids showed reduced abundance of synaptic proteins, fewer synaptic puncta and evidence of dysregulated retinoic acid and YAP1 signalling. Notably, most disease-associated alterations occurred independently of changes in transcript or protein abundance, indicating that key aspects of schizophrenia biology are encoded in protein state rather than expression level. These findings identify sex-specific dysregulation of protein state as a major molecular feature of schizophrenia and demonstrate the value of multi-layer proteomic approaches for uncovering disease mechanisms missed by transcriptomics alone.

neuroscience↗

Isolation and characterization of synaptic structures from human neural organoids

Human neural organoids (NOs) provide a powerful platform for investigating synaptic development and dysfunction during early neurodevelopment. However, methodologies for isolating functional synaptic structures from these models remain limited. Here, we present a differential centrifugation protocol enabling the enrichment of growth cone particles (GCPs) and immature synaptosomes from air-liquid interface cerebral organoids (ALI-COs) at distinct developmental stages (day 90 and 150). Notably, the method avoids density gradients, requires minimal starting material while maintaining reproducibility across human and murine tissues. Quantitative proteomic profiling revealed significant enrichment of growth cone markers (e.g. GAP43) and classical synaptosomal proteins (e.g. PCLO, BSN, SYN1). Transmission electron microscopy (TEM) confirmed the presence of membrane-enclosed GCPs with fibrous content and mitochondria in day 90 isolates, and immature synaptosomes containing synaptic vesicles on day 150. Functional viability of both types of synaptic structures was demonstrated through KCl-induced depolarization, which triggered phosphorylation changes in growth cone proteins (GAP43, MARCKS, MARCKSL1), cytoskeletal regulators (DCLK1, SHTN1, MARK4, MAP1B) and protein kinases (CAMK2G, PRKCE) in day 90 GCPs, as well as classical synaptic vesicle cycle proteins (SYN1, DNM1, RPH3A) at day 150. Overall, this study establishes a centrifugation-based protocol for isolating growth cones and immature synapses from human organoids, capturing key stages of synaptic development and enabling scalable, patient-compatible models to study synaptic function and dysfunction in neurodevelopmental and neurodegenerative disorders.

neuroscience↗

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Multi-omic characterization and integration remains a challenge due to data complexity and sparsity. Addressing this, our study introduces an unsupervised deep learning model, MOVE (Multi-Omic Variational Encoder), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). Harnessing orthogonal multi-omic information, this model successfully generates molecular and phenotypic profiles, resulting in an increase of 32.7% in the number of multi-omic profiles and thereby generating a complete DepMap for 1,523 cancer cell lines. The synthetically enhanced data increases statistical power, uncovering less studied mechanisms associated with drug resistance, and refines the identification of genetic associations and clustering of cancer cell lines. By applying SHAP for model interpretation, MOVE reveals multi-omic features essential for cell clustering and biomarker identification related to drug and gene dependencies. This understanding is crucial for the development of much-needed, effective strategies in prioritizing cancer targets.

systems biology↗

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↗

Dynamin 1xA interacts with Endophilin A1 via its spliced long C-terminus for ultrafast endocytosis

Dynamin 1 (Dyn1) has two major splice variants, xA and xB, with unique C-terminal extensions of 20 and 7 amino acids, respectively. Of these, only Dyn1xA is enriched at endocytic zones and accelerates vesicle fission during ultrafast endocytosis. Here, we report that the long tail variant, Dyn1xA, achieves this localization by preferentially binding to Endophilin A through a newly defined Class II binding site overlapping with its extension, at a site spanning the splice boundary. Endophilin binds this site at higher affinity than the previously reported site, and this affinity is determined by amino acids outside the binding sites acting as long distance elements within the xA tail. Their interaction is regulated by the phosphorylation state of two serine residues specific to the xA variant. Dyn1xA and Endophilin colocalize in patches near the active zone of synapses. Mutations selectively disrupting Endophilin binding to the long extension cause Dyn1xA mislocalization along axons. In these mutants, endocytic pits are stalled on the plasma membrane during ultrafast endocytosis. These data suggest that the specificity for ultrafast endocytosis is defined by the phospho-regulated interaction of Endophilin A through a newly identified site of Dyn1xAs long tail.

cell biology↗

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↗

Transformer-based deep learning integrates multi-omic data with cancer pathways

Multi-omic data analysis incorporating machine learning has the potential to significantly improve cancer diagnosis and prognosis. Traditional machine learning methods are usually limited to omic measurements, omitting existing domain knowledge, such as the biological networks that link molecular entities in various omic data types. Here we develop a Transformer-based explainable deep learning model, DeePathNet, which integrates cancer-specific pathway information into multi-omic data analysis. Using a variety of big datasets, including ProCan-DepMapSanger, CCLE, and TCGA, we demonstrate and validate that DeePathNet outperforms traditional methods for predicting drug response and classifying cancer type and subtype. Combining biomedical knowledge and state-of-the-art deep learning methods, DeePathNet enables biomarker discovery at the pathway level, maximizing the power of data-driven approaches to cancer research. DeePathNet is available on GitHub at https://github.com/CMRI-ProCan/DeePathNet. HighlightsO_LIDeePathNet integrates biological pathways for enhanced cancer analysis. C_LIO_LIDeePathNet utilizes Transformer-based deep learning for superior accuracy. C_LIO_LIDeePathNet outperforms existing models in drug response prediction. C_LIO_LIDeePathNet enables pathway-level biomarker discovery in cancer research. C_LI

bioinformatics↗

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↗

The Mammalian Cytosolic Thioredoxin Reductase Pathway Acts via a Membrane Protein to Reduce ER-localised Proteins

Folding of proteins entering the mammalian secretory pathway requires the insertion of the correct disulfide bonds. Disulfide formation involves both an oxidative pathway for their insertion and a reductive pathway to remove incorrectly formed disulfides. Reduction of these disulfides is critical for correct folding and degradation of misfolded proteins. Previously, we showed that the reductive pathway is driven by NADPH generated in the cytosol. Here, by reconstituting the pathway using purified proteins and ER microsomal membranes, we demonstrate that the thioredoxin reductase system provides the minimal cytosolic components required for reducing proteins within the ER lumen. In particular, saturation of the pathway and its protease sensitivity demonstrates the requirement for a membrane protein to shuttle electrons from the cytosol to the ER lumen. These results provide compelling evidence for the critical role of the cytosol in regulating ER redox homeostasis to ensure correct protein folding and to facilitate the degradation of misfolded ER proteins.

cell biology↗