Biology subjects

Mateo, L.

Publications and source records attributed to Mateo, L..

6 recordsLinked to original sources

bioRxiv2024

Systematic study of hybrid triplex topology and stability suggests a general triplex-mediated regulatory mechanism

By combining in-silico, biophysical and in-cellulo experiments, we decipher the topology, physical and potential biological properties of hybrid-parallel nucleic acids triplexes; an elusive structure at the basis of life. We found that hybrid triplex topology follows a stability order: r(Py)-d(Pu){middle dot}r(Py)> r(Py)-d(Pu){middle dot}d(Py)> d(Py)-d(Pu){middle dot}d(Py)> d(Py)-d(Pu){middle dot}r(Py). The r(Py)-d(Pu){middle dot}d(Py) triplex is expected to be the preferred in the cell as it avoids the need to open the duplex reducing the torsional stress required for triplex formation in the r(Py)-d(Pu){middle dot}r(Py) topology. Upon a massive collection of melting data, we have created the first predictor for hybrid triplex stability. Leveraging this predictor, we conducted a comprehensive scan to assess the likelihood of the human genome and transcriptome to engage in triplex formation. Our findings unveil a remarkable inclination - of both the human genome and transcriptome - to generate hybrid triplex formation, particularly within untranslated (UTRs) and regulatory regions, thereby corroborating the existence of a triplex-mediated regulatory mechanism. Furthermore, we found a correlation between nucleosome linkers and TFS which agree with a putative role of triplexes in arranging chromatin structure and local/global level.

molecular biology↗

bioRxiv2022

The efficacy of chemotherapy is limited by intratumoural senescent cells that persist through the upregulation of PD-L2

Anti-cancer therapies often result in a subset of surviving cancer cells that undergo therapy-induced senescence (TIS). Senescent cancer cells strongly modify the intratumoural microenvironment favoring immunosuppression and, thereby, tumour growth. An emerging strategy to optimise current therapies is to combine them with treatments that eliminate senescent cells. To this end, we undertook an unbiased proteomics approach to identify surface markers contributing to senescent cells immune evasion. Through this approach, we discovered that the immune checkpoint inhibitor PD-L2, but not PD-L1, is upregulated across multiple senescent human and murine cells. Importantly, blockade of PD-L2 strongly synergises with genotoxic chemotherapy, causing remission of solid tumours in mice. We show that PD-L2 inhibition prevents the persistence of chemotherapy-induced senescent cells, which exert cell-extrinsic immunomodulatory actions. In particular, upon chemotherapy, tumours deficient in PD-L2 fail to produce cytokines of the CXCL family, do not recruit myeloid-derived suppressor cells (MDSCs) and are eliminated in a CD8 T cell-dependent manner. We conclude that blockade of PD-L2 improves chemotherapy efficacy by reducing the intratumoural burden of senescent cells and their associated recruitment of immunosuppressive cells. These findings provide a novel strategy to exploit vulnerabilities arising in tumour cells as a result of therapy-induced damage and cellular senescence.

cancer biology↗

bioRxiv2021

Identification and drug-induced reversion of molecular signatures of Alzheimers disease onset and progression in AppNL-G-F, AppNL-F and 3xTg-AD mouse models

Alzheimers disease (AD) is the most common form of dementia. Over fifty years of intense research have revealed many key elements of the biology of this neurodegenerative disorder. However, our understanding of the molecular bases of the disease is still incomplete, and the medical treatments available for AD are mainly symptomatic and hardly effective. Indeed, the robustness of biological systems has revealed that the modulation of a single target is unlikely to yield the desired outcome and we should therefore move from gene-centric to systemic therapeutic strategies. Here we present the complete characterization of three murine models of AD at different stages of the disease (i.e. onset, progression and advanced). To identify genotype-to-phenotype relationships, we combine the cognitive assessment of these mice with histological analyses and full transcriptional and protein quantification profiling of the hippocampus. Comparison of the gene and protein expression trends observed in AD progression and physiological aging revealed certain commonalities, such as the upregulation of microglial and inflammation markers. However, although AD models show accelerated aging, other factors specifically associated with A{beta} pathology are involved. Despite the clear correlation between mRNA and protein levels of the dysregulated genes, we discovered a few proteins whose abundance increases with AD progression, while the corresponding transcript levels remain stable. Indeed, we show that at least two of these proteins, namely lfit3 and Syt11, co-localize with A{beta} plaques in the brain. Finally, we derived specific A{beta}-related molecular AD signatures and looked for drugs able to globally revert them. We found two NSAIDs (dexketoprofen and etodolac) and two anti-hypertensives (penbutolol and bendroflumethiazide) that overturn the cognitive impairment in AD mice while reducing A{beta} plaques in the hippocampus and partially restoring the physiological levels of AD signature genes to wild-type levels. TeaserThe comprehensive characterization of three AD mouse models reveals disease signatures that we used to identify approved drugs able to modify the etiology of the pathology and overturn cognitive impairment.

neuroscience↗

bioRxiv2020

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

cancer biology↗

bioRxiv2019

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Identification of actionable genomic vulnerabilities is the cornerstone of precision oncology. Based on a large-scale drug screening in patient derived-xenografts, we uncover connections between driver gene alterations, derive Driver Co-Occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug response predictors attained an average balanced accuracy of 58% in a cross-validation setting, which rose to a 66% for the subset of high-confidence predictions. Morevover, we experimentally validated 12 out of 14 de novo predictions in mice. Finally, we adapted our strategy to obtain drug-response models from patients progression free survival data. By revealing unexpected links between oncogenic alterations, our strategy can increase the clinical impact of genomic profiling.

cancer biology↗

bioRxiv2018

Genome-wide patterns of local adaptation in Drosophila melanogaster: adding intra European variability to the map

Signatures of spatially varying selection have been investigated both at the genomic and transcriptomic level in several organisms. In Drosophila melanogaster, the majority of these studies have analyzed North American and Australian populations, leading to the identification of several loci and traits under selection. However, populations in these two continents showed evidence of admixture that likely contributed to the observed population differentiation patterns. Thus, disentangling demography from selection is challenging when analyzing these populations. European populations could be a suitable system to identify loci under spatially varying selection provided that no recent admixture from African populations would have occurred. In this work, we individually sequence the genome of 42 European strains collected in populations from contrasting environments: Stockholm (Sweden), and Castellana Grotte, (Southern Italy). We found low levels of population structure and no evidence of recent African admixture in these two populations. We thus look for patterns of spatially varying selection affecting individual genes and gene sets. Besides single nucleotide polymorphisms, we also investigate the role of transposable elements in local adaptation. We concluded that European populations are a good dataset to identify loci under spatially varying selection. The analysis of the two populations sequenced in this work in the context of all the available D. melanogaster data allowed us to pinpoint genes and biological processes relevant for local adaptation. Identifying and analyzing populations with low levels of population structure and admixture should help to disentangle selective from non-selective forces underlying patterns of population differentiation in other species as well.

evolutionary biology↗