Biology subjects

Barbero, R.

Publications and source records attributed to Barbero, R..

6 recordsLinked to original sources

bioRxiv2025

Demonstration of SLU7 as a new pan-cancer target

Cancer treatment remains challenging due to heterogeneous responses to immunotherapy across patients and tumor types. Innovative strategies are required to overcome immune evasion. We have identified the splicing factor SLU7 as essential for the survival of cancer cells from diverse origins. SLU7 knockdown induces R-loop accumulation, transcription-dependent genomic instability, DNA damage, and replication catastrophe, together with aberrant splicing and inhibition of nonsense-mediated mRNA decay (NMD) and/or DNA methylation. These alterations lead to the expression of neoantigens, interferon B1, endogenous retroviruses, and cancer-testis antigens, which would enhance tumor immunogenicity. Therefore, we propose SLU7 targeting as a dual-action therapy, combining direct tumor suppression with immune activation. Using various murine cancer models, including orthotopic liver tumors, and multiple molecular strategies--such as inducible CRISPR/Cas9, systemic delivery of chimeric siSLU7-nucleolin aptamers (APTASLU), and intratumoral injection of siSLU7-loaded nanoparticles--we show that distinct siSLU7 sequences and delivery platforms effectively inhibit tumor growth. Furthermore, SLU7 silencing synergizes with immune checkpoint inhibitors, amplifying anti-tumor responses. Our in vivo data demonstrate that SLU7 is a promising, versatile target for diverse cancers. Its multimodal mechanism offers potential to overcome tumor heterogeneity, reverse immune tolerance, and enhance immunotherapy efficacy.

cancer biology↗

bioRxiv2025

Histone methyl-transferase G9a inhibition boosts the efficacy of immune checkpoint inhibitors in experimental hepatocellular carcinoma

Background and AimsImmune checkpoint inhibitors (ICI) have revolutionized cancer therapy. Yet, their efficacy in hepatocellular carcinoma (HCC) remains limited, partly due to tumor-intrinsic mechanisms of immune evasion. This study focused on the identification of potential epigenetic drivers of immune resistance in HCC evaluating the therapeutic potential of targeting the histone methyltransferase G9a (EHMT2). Approach and ResultsWe analyzed G9a expression across multiple human HCC cohorts and found that elevated G9a levels were inversely correlated with the most relevant immune-related gene expression signatures predictive of ICI responsiveness. Using HCC cell lines and orthotopic models implemented in immunocompetent mice, we assessed the effects of pharmacologic inhibition of G9a with two innovative epigenetic inhibitors, CM272 and EZM8266. G9a blockade enhanced tumor cell immunogenicity by restoring IFN{gamma} responsiveness, increasing MHC-I surface expression, and promoting chemokine-mediated (CXCL10) recruitment of T cells. Mechanistically, G9a inhibition induced a viral mimicry response through derepressing endogenous retroviral elements and the accumulation of cytosolic double-stranded RNA. In vivo, G9a inhibition synergized with anti-PD-1 therapy to suppress tumor growth, significantly enhancing CD8 T cell infiltration. Notably, in a clinically-relevant post-hepatectomy HCC recurrence model, the combination therapy overcame immune resistance. ConclusionsG9a functions as a central epigenetic barrier to antitumor immunity in HCC. Pharmacologic G9a inhibition reprograms the tumor microenvironment, enhances immunogenicity, and sensitizes tumors to ICIs. These findings provide strong preclinical rationale for integrating G9a-targeted therapies with immunotherapy, particularly in perioperative settings.

pharmacology and toxicology↗

bioRxiv2021

Nanotrap Particles Improve Nanopore Sequencing of SARS-CoV-2 and Other Respiratory Viruses

Presented here is a magnetic hydrogel particle enabled workflow for capturing and concentrating SARS-CoV-2 from diagnostic remnant swab samples that significantly improves sequencing results using the Oxford Nanopore Technologies MinION sequencing platform. Our approach utilizes a novel affinity-based magnetic hydrogel particle, circumventing low input sample volumes and allowing for both rapid manual and automated high throughput workflows that are compatible with nanopore sequencing. This approach enhances standard RNA extraction protocols, providing up to 40x improvements in viral mapped reads, and improves sequencing coverage by 20-80% from lower titer diagnostic remnant samples. Furthermore, we demonstrate that this approach works for contrived influenza virus and respiratory syncytial virus samples, suggesting that it can be used to identify and improve sequencing results of multiple viruses in VTM samples. These methods can be performed manually or on a KingFisher Apex system.

microbiology↗

bioRxiv2020

Nanotrap(R) particles improve detection of SARS-CoV-2 for pooled sample methods, extraction-free saliva methods, and extraction-free medium methods

Here we present a rapid and versatile method for capturing and concentrating SARS-CoV-2 from transport medium and saliva using affinity-capture magnetic hydrogel particles. We demonstrate that the method concentrates virus prior to RNA extraction, thus significantly improving detection of the virus using a real-time RT-PCR assay across a range of viral titers, from 100 to 1,000,000 viral copies/mL; in particular, detection of virus in low viral load samples is enhanced when using the method coupled with the IDT 2019-nCoV CDC EUA Kit. This method is compatible with commercially available nucleic acid extraction kits, as well with a simple heat and detergent method. Using transport medium diagnostic remnant samples that previously had been tested for SARS-CoV-2 using either the Abbott RealTime SARS-CoV-2 EUA Test (n=14) or the Cepheid Xpert Xpress SARS-CoV-2 EUA Test (n=35), we demonstrate that our method not only correctly identifies all positive samples (n = 17) but also significantly improves detection of the virus in low viral load samples. The average improvement in cycle threshold (Ct) value as measured with the IDT 2019-nCoV CDC EUA Kit was 3.1; n = 10. Finally, to demonstrate that the method could potentially be used to enable pooled testing, we spiked infectious virus or a confirmed positive diagnostic remnant sample into 5 mL and 10 mL of negative transport medium and observed significant improvement in the detection of the virus from those larger sample volumes.

microbiology↗

bioRxiv2020

Prediction of regional wildfire activity with a probabilistic Bayesian framework

O_LIModelling wildfire activity is crucial for informing science-based risk management and understanding fire-prone ecosystem functioning worldwide. Models also help to disentangle the relative roles of different factors, to understand wildfire predictability or to provide insights into specific events. C_LIO_LIHere, we develop a two-component Bayesian hierarchically-structured probabilistic model of daily fire activity, which are modelled as the outcome of a marked point process in which individual fires are the points (occurrence component) and the fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the Stochastic Partial Differential Equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and Generalized-Pareto distributions, also adjusted with INLA. The Fire Weather Index (FWI) and Forest Area are the main explanatory variables. Seasonal and spatial residuals as well as a post-2003 effect are included to improve the consistency of the relationship between climate and fire occurrence, in accordance with parsimonious criteria. C_LIO_LIA set of 1000 simulations of the posterior model of fire activity is evaluated at various temporal and spatial scales in Mediterranean France. The number of escaped fires ([≥]1ha) across the region can be coarsely reproduced at the daily scale, and is more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10 to 100 ha) can be predicted as well, but the accuracy decays with size, as the model uncertainty increases with event rareness. Local predictions of fire numbers or burnt areas likewise require a longer aggregation period to maintain model accuracy. C_LIO_LIRegarding the year 2003 -which was characterized by an extreme burnt area in France associated with a heat wave-, the estimation of the number of escaped fires was consistent with observations, but the model systematically underrepresents larger fires and burnt areas, which suggests that the FWI does not consistently rate the danger of large fire occurrence during heat waves. C_LIO_LIOur study sheds new light on the stochastic processes underlying fire hazard, and is promising for predicting and projecting future fire hazard in the context of climate change. C_LI

bioengineering↗

bioRxiv2020

Increased likelihood of heat-induced large wildfires in the Mediterranean Basin

Wildfire activity is expected to increase across the Mediterranean Basin because of climate change. However, the effects of future climate changes on the combinations of atmospheric conditions that promote large wildfires remain largely unknown. Using a fire-weather based classification of wildfires, we show that future climate scenarios point to an increase in the frequency and severity of two heat-induced fire-weather types that have been responsible for a majority of record-breaking wildfire events. Heat-induced fire-weather types are characterized by compound dry warm conditions and occur in the summer during heatwaves, either under moderate (sudden heatwave type) or intense (hot drought type) drought. Heat-induced fire weather is projected to increase in frequency by 14% by the end of the century (2071-2100) under the RCP4.5 scenario, and by 30% under the RCP8.5. These findings suggest that the frequency and extent of large wildfires will increase throughout the Mediterranean Basin, with far-reaching impacts.

ecology↗