Biology subjects

Nikolaou, N.

Publications and source records attributed to Nikolaou, N..

9 recordsLinked to original sources

bioRxiv2026

An SNRNP70-eGFP knock-in zebrafish line reveals the physiological localisation and dynamics of endogenous SNRNP70 during development

SNRNP70 is a core spliceosome RNA-binding protein best known for its essential role in nuclear pre-mRNA splicing. Although traditionally associated with nuclear RNA processing, previous studies have identified important extranuclear functions for SNRNP70 in neurons, including roles in mRNA stability, localisation, and axonal transport. Yet, much of our understanding of SNRNP70 localisation has relied on overexpression or transgenic approaches, leaving a critical gap in our knowledge of where endogenous SNRNP70 resides and how it behaves in living neurons under physiological expression conditions. Here, we address this limitation by establishing and validating a novel zebrafish SNRNP70-eGFP CRISPR knock-in line, enabling direct visualisation of the endogenous protein. eGFP was fused to the C-terminus of endogenous SNRNP70 while retaining the native 3' untranslated region, preserving key regulatory features of the endogenous locus. We demonstrate that the knock-in faithfully reports endogenous SNRNP70 expression and reveals widespread physiological localisation throughout the developing nervous system, including prominent enrichment within axonal and synaptic compartments. Crucially, live in vivo imaging reveals that endogenous SNRNP70 is dynamically localised within neuronal mRNP granules, providing direct evidence of its physiological behaviour in these structures without the confounding effects of protein overexpression. Proximity ligation analyses further demonstrates associations between endogenous SNRNP70 and PABPC1B, FUS, and UPF1, which are established neuronal mRNP granule components. Together, our work provides a validated genetic and imaging resource for investigating SNRNP70 at endogenous levels in the living nervous system. By overcoming key limitations of conventional transgenic and overexpression-based approaches, the SNRNP70-eGFP knock-in enables physiological analysis of SNRNP70 localisation and dynamics and reveals its prominent and dynamic organisation within neuronal mRNP granules. More broadly, this work highlights how endogenous fluorescent tagging can provide a versatile platform for resolving the spatial organisation of RNA-binding proteins in living neurons under physiological expression conditions and provide new insight into the regulation of neuronal mRNA fate.

neuroscience↗

bioRxiv2026

SNRNP70 interacts with TDP-43 to promote RNP granule localisation and regulate motor neuron development

SNRNP70 is a core spliceosomal protein that localises to both the nucleus and cytoplasm. Previous studies have implicated SNRNP70 in regulating axonal stability and the transport of specific mRNAs during motor neuron development in zebrafish. Although the molecular functions and protein interactions of SNRNP70 in pre-mRNA splicing are well established, the mechanisms underlying its cytoplasmic functions remain poorly understood. Here, we show that SNRNP70 and TDP-43 exhibit similar localisation patterns in developing and mature neurons and co-associate in both nuclear and non-nuclear compartments, including axonal projections. We identify a functional interaction between SNRNP70 and TDP-43 that is essential for motor neuron development and demonstrate that the recruitment of SNRNP70 to cytoplasmic ribonucleoprotein (RNP) granules depends on TDP-43. These findings identify a previously unrecognised cytoplasmic function of TDP-43 in directing SNRNP70-containing RNP granule assembly, thereby linking TDP-43 to the splicing-independent functions of SNRNP70 during motor neuron development.

neuroscience↗

bioRxiv2025

iPSC-derived hepatocytes accurately recapitulate population diversity in alpha-1-antitrypsin deficiency and offer a novel in vitro model for large-scale drug efficacy screening studies.

BackgroundAlpha-1 antitrypsin deficiency (A1ATD) is a hereditary recessive disorder caused by mutations in the SERPINA1 gene. It is a clinically under-recognised disease characterised by low circulating A1AT levels and intracellular accumulation of misfolded A1AT in hepatocytes. Deposition of excessive abnormal A1AT in the liver leads to liver failure, yet no specific treatments are available due to the lack of physiologically relevant disease modelling platforms. MethodsWe have hypothesised that human induced pluripotent stem cell (iPSC)-derived hepatocytes can provide an efficient platform to study A1ATD. Using CRISPR/Cas9, we have generated wild-type and A1ATD iPSC-derived hepatocytes (Opti-HEP) from healthy and A1ATD donors and developed a bioassay that mimics the accumulation of misfolded A1AT in the liver. Responses to the reference drug carbamazepine (CBZ), known to reduce intracellular misfolded A1AT levels, and RNA-based therapeutics were subsequently investigated. ResultsAll lines successfully differentiated into hepatocytes as measured by comparable key hepatic and disease markers to those seen in primary human hepatocytes. The diseased lines displayed increased intracellular accumulation of misfolded A1AT compared to isogenic controls. Diseased cell lines showed significant decreases in intracellular accumulation of polymeric A1AT following transfection with RNA-based therapeutics, but a differential response upon treatment with CBZ. ConclusionWe have developed a specific and robust in vitro model of A1ATD that recapitulates disease pathophysiology and responds to small molecule-based treatments and advanced therapeutic strategies. These data demonstrate the suitability of this model for large-scale efficacy screening studies for the treatment of A1ATD and help pave the way towards the development of novel therapies.

cell biology↗

bioRxiv2024

Development of optimised human iPSC-derived hepatocytes with improved liver function for in vitro metabolic disease modelling and toxicity studies

Background & AimsLiver disease is a rising cause of mortality worldwide. Primary human hepatocytes (PHH) and hepatocellular cancer cells are currently used in drug development, however, they come with limitations, including limited supply, rapid loss of function, and tumorigenic origin. In addition, current iPSC differentiation protocols lead to the generation of hepatocyte-like cells with compromised liver-related features. We hypothesised that optimisation of iPSC differentiation protocols can lead to the generation of hepatocyte-like cells with improved metabolic functionality for disease modelling and toxicity screening studies. MethodsHealthy human iPSCs were differentiated to hepatocyte-like cells (Opti-HEP) using a novel 3-step differentiation protocol. Hepatocyte functionality was assessed, including liver maturity marker expression, urea synthesis, de novo gluconeogenesis, and CYP450 expression, activity, and induction. Suitability of Opti-HEP to predict drug-induced liver injury (DILI) was evaluated by cell viability assays. CRISPR/Cas9 gene editing was employed to generate in vitro inherited metabolic disease models. ResultsOpti-HEP expressed similar liver maturity marker levels to those seen in primary human hepatocytes (PHH), in addition to functional urea and gluconeogenesis pathways. CYP450 expression and activity were comparable between Opti-HEP and PHH, with both cell types showing similar levels of CYP3A4 induction upon 1,25-hydroxy-vitamin D3 treatment. Opti-HEP accurately predicted DILI, following treatment with 7 drugs of known DILI liability. CRISPR-derived Opti-HEP harbouring mutations for inherited metabolic disorders (Ornithine Transcarbamylase Deficiency, Progressive Familial Intrahepatic Cholestasis Type 2, Citrullinemia Type 1) recapitulated key pathophysiological disease features, including reduced protein expression, impaired urea secretion, and bile acid transport. ConclusionsWe demonstrate the generation of optimised iPSC-derived hepatocytes with enhanced liver functionality that is comparable to PHH. These data alongside the expansion capacity and amenability of these cells highlight the opportunities this model can offer in the space of disease modelling and large-scale drug efficacy and hepatotoxicity screening.

cell biology↗

bioRxiv2024

Towards a Survival Risk Prediction Model for Metastatic NSCLC Patients on Durvalumab Using Whole-Lung CT Radiomics

BackgroundExisting criteria for predicting patient survival from immunotherapy are primarily centered on the PD-L1 status of patients. We tested the hypothesis that noninvasively captured baseline whole-lung radiomics features from CT images, baseline clinical parameters, combined with advanced machine learning approaches, can help to build models of patient survival that compare favorably with PD-L1 status for predicting less-than-median-survival risk in the metastatic NSCLC setting for patients on durvalumab. With a total of 1062 patients, inclusive of model training and validation, this is the largest such study yet. MethodsTo ensure a sufficient sample size, we combined data from treatment arms of three metastatic NSCLC studies. About 80% of this data was used for model training, and the remainder was held-out for validation. We first trained two independent models; Model-C trained to predict survival using clinical data; and Model-R trained to predict survival using whole-lung radiomics features. Finally, we created Model-C+R which leveraged both clinical and radiomics features. ResultsThe classification accuracy (for median survival) of Model-C, Model-R, and Model-C+R was 63%, 55%, and 68% respectively. Sensitivity analysis of survival prediction across different training and validation cohorts showed concordance indices ([95 percentile]) of 0.64 ([0.63, 0.65]), 0.60 ([0.59, 0.60]), and 0.66 ([0.65,0.67]), respectively. We additionally evaluated generalization of these models on a comparable cohort of 144 patients from an independent study, demonstrating classification accuracies of 65%, 62%, and 72% respectively. ConclusionMachine Learning models combining baseline whole-lung CT radiomic and clinical features may be a useful tool for patient selection in immunotherapy. Further validation through prospective studies is needed.

bioinformatics↗

bioRxiv2024

Quantifying the advantage of multimodal data fusion for survival prediction in cancer patients

The last decade has seen an unprecedented advance in technologies at the level of high-throughput molecular assays and image capturing and analysis, as well as clinical phenotyping and digitization of patient data. For decades, genotyping (identification of genomic alterations), the casual anchor in biological processes, has been an essential component in interrogating disease progression and a guiding step in clinical decision making. Indeed, survival rates in patients tested with next-generation sequencing have been found to be significantly higher in those who received a genome-guided therapy than in those who did not. Nevertheless, DNA is only a small part of the complex pathophysiology of cancer development and progression. To assess a more complete picture, researchers have been using data taken from multiple modalities, such as transcripts, proteins, metabolites, and epigenetic factors, that are routinely captured for many patients. Multimodal machine learning offers the potential to leverage information across different bioinformatics modalities to improve predictions of patient outcome. Identifying a multiomics data fusion strategy that clearly demonstrates an improved performance over unimodal approaches is challenging, primarily due to increased dimensionality and other factors, such as small sample sizes and the sparsity and heterogeneity of data. Here we present a flexible pipeline for systematically exploring and comparing multiple multimodal fusion strategies. Using multiple independent data sets from The Cancer Genome Atlas, we developed a late fusion strategy that consistently outperformed unimodal models, clearly demonstrating the advantage of a multimodal fusion model.

bioinformatics↗

bioRxiv2021

Akr1d1-/- mice have a sexually dimorphic metabolic phenotype with reduced fat mass, increased insulin sensitivity and hypertriglyceridemia in males

BackgroundSteroid 5{beta}-reductase (AKR1D1) plays important roles in hepatic glucocorticoid clearance and bile acid synthesis. Glucocorticoids and bile acids are potent metabolic regulators, but whether AKR1D1 controls metabolic phenotype in vivo is unknown. MethodsAkr1d1-/-mice were generated on a C57BL/6 background. Liquid chromatography / mass spectrometry, metabolomic and transcriptomic approaches were used to determine effects on glucocorticoid and bile acid homeostasis. Metabolic phenotypes including body weight and composition, lipid homeostasis, glucose tolerance and insulin sensitivity were evaluated. Molecular changes were assessed by RNASeq and western blotting. Male Akr1d1-/-mice were challenged with a 60% high fat diet. ResultsAkr1d1-/-mice had a sex specific metabolic phenotype. At 30-weeks of age male, but not female, Akr1d1-/-mice were more insulin sensitive and had reduced lipid accumulation in the liver and adipose tissue, concomitant with hypertriglyceridemia and increased intramuscular triacylglycerol. This phenotype was underpinned by sexually dimorphic changes in bile acid metabolism and composition, but without overt effects on glucocorticoid action. Male Akr1d1-/-mice were not protected against diet induced obesity and insulin resistance. ConclusionThis study shows that AKR1D1 controls bile acid homeostasis in vivo and that altering its activity can affect insulin sensitivity and lipid homeostasis in a sex dependent manner.

physiology↗

bioRxiv2020

Differential activity and expression of human 5β-reductase (AKR1D1) splice variants

Steroid hormones, including glucocorticoids and androgens, exert a wide variety of effects in the body across almost all tissues. The steroid A-ring 5{beta}-reductase (AKR1D1) is expressed in human liver and testes, and three splice variants have been identified (AKR1D1-001, AKR1D1-002, AKR1D1-006). Amongst these, AKR1D1-002 is the best described; it modulates steroid hormone availability and catalyses an important step in bile acid synthesis. However, specific activity and expression of AKR1D1-001 and AKR1D1-006 are unknown. AKR1D1-002, AKR1D1-001 and AKR1D1-006 were measured in human liver biopsies and human hepatoma cell lines by qPCR. Three-dimensional (3D) structures of AKR1D1 variants were determined using in silico approaches. AKR1D1 variants were over-expressed in HEK293 cells, and successful overexpression confirmed by qPCR and western blotting. Steroid hormone clearance was measured by mass spectrometry and ELISA, and steroid receptor activation determined by luciferase reporter assays. AKR1D1-002 and AKR1D1-001 are expressed in human liver, and only AKR1D1-006 is expressed in human testes. Following over-expression in HEK293 cells, AKR1D1-001 and AKR1D1-006 protein levels were lower than AKR1D1-002, but significantly increased following treatment with the proteasomal inhibitor, MG-132. AKR1D1-002 efficiently metabolised glucocorticoids and androgens and decreased receptor activation. AKR1D1-001 and AKR1D1-006 poorly metabolised dexamethasone, but neither protein metabolised cortisol, prednisolone or testosterone. We have demonstrated the differential expression and role of AKR1D1 splice variants to regulate steroid hormone clearance and receptor activation. AKR1D1-002 is the predominant functional protein in steroidogenic and metabolic tissues. In addition, AKR1D1-001 and AKR1D1-006 may have a limited role in the regulation of synthetic glucocorticoid action.

molecular biology↗

bioRxiv2020

Cytoplasmic pool of spliceosome protein SNRNP70 regulates the axonal transcriptome and development of motor connectivity

Regulation of pre-mRNA splicing and polyadenylation plays a profound role in neurons by diversifying the proteome and modulating gene expression during development and in response to physiological cues. Although most pre-mRNA processing reactions are thought to occur in the nucleus, numerous splicing regulators are also found in neurites. Here, we show that U1-70K/SNRNP70, a major spliceosomal component, localizes in RNA-associated granules in axons. We identify the cytoplasmic pool of SNRNP70 as an important local regulator of motor axonal growth, nerve-dependent acetylcholine receptor (AChR) clustering and neuromuscular synaptogenesis. This cytoplasmic pool has a protective role for a limited number of axonal transcripts preventing them from degradation. Moreover, non-nuclear SNRNP70 is able to locally regulates splice variants of transcripts such as agrin, thereby locally controlling formation of synapses. Our results point to an unexpected, yet essential, function of local SNRNP70 in axonal development and indicate a role of splicing factors in local RNA metabolism during establishment and maintenance of neuronal connectivity.

neuroscience↗