Biology subjects

Rayner, S.

Publications and source records attributed to Rayner, S..

6 recordsLinked to original sources

bioRxiv2025

Tankyrase inhibition demonstrates anti-fibrotic effects in preclinical pulmonary fibrosis models

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with limited treatment options. Although transforming growth factor beta 1 (TGFB1, TGF{beta}) is a key driver of fibrosis, additional signaling pathways, including wingless-type mammary tumor virus integration site (WNT)/{beta}-catenin and yes-associated protein 1 (YAP), contribute to IPF pathogenesis. Clinical data indicate that inhibition of TGF{beta} alone provides limited efficacy or is associated with toxicity, underscoring the need for alternative therapeutic approaches. Tankyrase (TNKS) 1 and 2 are post-translational regulators of WNT/{beta}-catenin and YAP signaling and therefore represent promising antifibrotic targets. OM-153, a potent and selective TNKS inhibitor, exhibits pharmacological properties suitable for preclinical development in IPF. MethodsPrimary normal human lung fibroblasts (NHLF), Scar-in-a-Jar assays, lung-on-a-chip models, and precision-cut lung slices (PCLS) from non-pulmonary fibrosis (non-PF) tissue were stimulated with an IPF-relevant cytokine cocktail (IPF-RC) designed to accurately recapitulate the pro-fibrotic environment and compared to TGF{beta}. These models, with bleomycin-challenged mice and PCLS from end-stage pulmonary fibrosis (PF) patients, were treated with OM-153. Fibrosis markers, extracellular matrix (ECM) components, and signaling pathway-specific gene expression or protein markers were assessed by real-time qRT-PCR, RNA sequencing, immunoblotting, ELISA, and immunofluorescence. ResultsOM-153 stabilized the direct TNKS targets axin 1 (AXIN1) and angiomotin-like 1 (AMOTL1), suppressed WNT/{beta}-catenin and YAP signaling. In parallel, it reduced profibrotic ECM expression across in vitro, in vivo, and ex vivo IPF models. ConclusionsSelective TNKS inhibition by OM-153 demonstrates broad antifibrotic activity in multiple preclinical models, supporting further development as a potential disease-modifying strategy for IPF. Shareable abstractOur findings show that the potent and selective TNKS inhibitor OM-153 suppresses WNT/{beta}-catenin and YAP signaling, reducing pro-fibrotic ECM expression in preclinical IPF models, supporting TNKS inhibition as a novel antifibrotic strategy.

cell biology↗

bioRxiv2024

Association Between Oral Microbiota and Close-Range Proximity in a Primary School

The microbiota is the ensemble of microorganisms inhabiting the human body and it deeply influences human health and well-being. Recent studies showed its interplay with social behavior, suggesting that part of the microbiota might be socially transmissible. In this work, we investigate the association between close-range proximity and the oral microbiota composition in a group of children attending primary school. Unlike most related studies, our cohort comprises non-cohabiting individuals, and we use high-resolution proximity sensors to objectively quantify close-range proximity. Our analysis shows that longer time spent in proximity between pairs of children correlates with a higher similarity between their oral microbiota composition. Building on previous studies on cohabiting individuals, our findings suggest that social contacts beyond the closest social circle may also shape oral microbiota composition, although longitudinal studies are needed to test this hypothesis.

microbiology↗

bioRxiv2024

Cell-Cell Fusion in NSCLC Confers a Fitness Benefit Under Drug Selection

Cell-cell fusion has been implicated in various physiological and pathological processes, including cancer progression. This study investigated the role of cell-cell fusion in non-small cell lung cancer (NSCLC), focusing on its contribution to chemoresistance and tumor evolution. By co-culturing drug-sensitive and drug-resistant NSCLC cell lines, we observed spontaneous cell-cell fusion events, particularly under gefitinib selection. These fused cells exhibited enhanced fitness and a higher degree of chemoresistance compared to parental lines across a panel of 12 chemotherapeutic agents. Further analysis, including fluorescence imaging and cell cycle analysis, confirmed nuclear fusion and increased DNA content in the fused cells. Bulk RNA sequencing revealed genomic heterogeneity in fused cells, including enrichment of gene sets associated with cell cycle progression and epithelial-mesenchymal transition, both of which are hallmarks of cancer. These findings demonstrate that cell-cell fusion can act as a novel source of chemotherapeutic resistance and further promote aggressive phenotypes in NSCLC, highlighting the potential of fusion as a therapeutic target.

cancer biology↗

bioRxiv2020

An extension to: Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. To assist researchers choose the most appropriate library preparation kit, we recently compared the performance of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. As before, performance was assessed with respect to sensitivity, reliability, titration response and differential expression. Despite NEXTflex employing partially-randomised adapter sequences to minimise bias, we reaffirm that biases in miRNA abundance are kit-specific, complicating the comparison of miRNA datasets generated using different kits.

molecular biology↗

bioRxiv2019

Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing is increasingly favoured to assay the presence and abundance of micro RNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. However, although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human biological total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers sequence (UMI) tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.

bioinformatics↗

bioRxiv2017

miRAW: A deep learning approach to predict miRNA targets by analyzing whole miRNA transcripts

AbstractMicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA \"seed region\" (nt 2 to 8) is required for functional targeting, but typically only identify {bsim}80% of known bindings. Recent studies have highlighted a role for the entire miRNA, suggesting that a more flexible methodology is needed.\n\nWe present a novel approach for miRNA target prediction based on Deep Learning (DL) which, rather than incorporating any knowledge (such as seed regions), investigates the entire miRNA and 3UTR mRNA nucleotides to learn a uninhibited set of feature descriptors related to the targeting process.\n\nWe collected more than 150,000 experimentally validated homo sapiens miRNA:gene targets and cross referenced them with different CLIP-Seq, CLASH and iPAR-CLIP datasets to obtain {bsim}20,000 validated miRNA:gene exact target sites. Using this data, we implemented and trained a deep neural network - composed of autoencoders and a feed-forward network - able to automatically learn features describing miRNA-mRNA interactions and assess functionality. Predictions were then refined using information such as site location or site accessibility energy.\n\nIn a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality. Predictions were then refined using information such as site location or site accessibility energy.\n\nIn a comparison using independent datasets, our DL approach consistently outperformed existing prediction methods, recognizing the seed region as a common feature in the targeting process, but also identifying the role of pairings outside this region. Thermodynamic analysis also suggests that site accessibility plays a role in targeting but that it cannot be used as a sole indicator for functionality.\n\nData and source code available at: https://bitbucket.org/account/user/bipous/projects/MIRAW\n\nAuthor summarymicroRNAs are small RNA molecules that regulate biological processes by binding to the 3'UTR of a gene and their dysregulation is associated with several diseases. Computationally predicting these targets remains a challenge as they only partially match their target and so there can be hundreds of targets for a single microRNA. Current tools assume that most of the knowledge defining a microRNA-gene interaction can be captured by analysing the binding produced in the seed region ({approx} the first 8nt in the miRNA). However, recent studies show that the whole microRNA can be important and form non-canonical targets. Here, we use a target prediction methodology that relies on deep neural networks to automatically learn the relevant features describing microRNA-gene interactions for predicting microRNA targets. This means we make no assumptions about what is important, leaving the task to the deep neural network. A key part of the work is obtaining a suitable dataset. Thus, we collected and curated more than 150,000 experimentally verified microRNA targets and used them to train the network. Using this approach, we are able to gain a better understanding of non-canonical targets and to improve the accuracy of state-of-the-art prediction tools.

bioinformatics↗