Biology subjects

Domingo, E.

Publications and source records attributed to Domingo, E..

14 recordsLinked to original sources

bioRxiv2026

A mathematical model for fitness effects on viral persistence

Persistent viral infections arise from complex interactions between viral replication, host cell responses, and ongoing viral evolution. A general framework linking viral fitness to persistence dynamics is lacking. Here, we develop for the first time a mathematical model of viral persistence that takes into consideration viral fitness variations. Essential to the model is the partition of the classical fitness parameter into three components: replicative, infective, and dispersal fitness. The model was initially inspired by a new experiment on hepatitis C virus (HCV) persistence, established in human hepatoma cells, also reported in this work. This experiment documents two strikingly different viral trajectories depending on the initial replicative fitness of the viral population used to establish persistence. The dynamical model describes the interactions among uninfected cells, infected cells, and infectious virions, and it incorporates, through a continuum, two alternative mechanisms of viral release from cells: budding and lysis. Analysis of the model reveals that viral fitness parameters organise infection outcomes into distinct dynamical regimes. Low replicative and dispersal fitness values lead to viral extinction, whereas high values enable persistence through either stable coexistence or recurrent infection waves. The space of fitness discloses a hierarchy among these parameters, with replicative and dispersal fitness being able to trigger important shifts in the outcome of the infection as opposed to infective fitness. The model identifies trade-offs between replication and dispersal that shape viral production and predicts slow dynamical regimes in which infection may persist despite low detectable viral loads. These dynamical transitions provide candidate mechanisms capable of generating the persistence patterns observed experimentally. Our results establish a computational framework linking multidimensional viral fitness to persistence dynamics and suggest general principles by which evolving RNA viruses transition between extinction (cell curing) and sustained persistence.

systems biology↗

bioRxiv2023

Innate immune receptor C5aR1 regulates cancer cell fate and can be targeted to improve radiotherapy in tumours with immunosuppressive microenvironments

An immunosuppressive microenvironment causes poor tumour T-cell infiltration and is associated with reduced patient overall survival in colorectal cancer. How to improve treatment responses in these tumours is still a challenge. Using an integrated screening approach to identify cancer-specific vulnerabilities, we identify complement receptor C5aR1 as a druggable target which when inhibited improves radiotherapy even in tumours displaying immunosuppressive features and poor CD8+ T-cell infiltration. While C5aR1 is well-known for its role in the immune compartment, we find that C5aR1 is also robustly expressed on malignant epithelial cells, highlighting potential tumour-cell specific functions. C5aR1 targeting results in increased NF-{kappa}B-dependent apoptosis specifically in tumours and not normal tissues; indicating that in malignant cells, C5aR1 primarily regulates cell fate. Collectively, these data reveal that increased complement gene expression is part of the stress response mounted by irradiated tumours and that targeting C5aR1 can improve radiotherapy even in tumours displaying immunosuppressive features.

cancer biology↗

bioRxiv2022

Biological misinterpretation of transcriptional signatures in tumour samples can unknowingly undermine mechanistic understanding and faithful alignment with preclinical data

Precise mechanism-based gene expression signatures (GESs) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signalling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumour samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumour stroma percentage can directly influence GESs, undermining the intended mechanistic signalling. Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single cell RNAseq and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely-used GESs to be influenced, or confounded, by stromal content in tumour tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumour models not fully recapitulating the stromal and immune microenvironment. As such, efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

cancer biology↗

bioRxiv2022

SARS-CoV-2 Point Mutation and Deletion Spectra, and Their Association with Different Disease Outcome

Mutant spectra of RNA viruses are important to understand viral pathogenesis, and response to selective pressures. There is a need to characterize the complexity of mutant spectra in coronaviruses sampled from infected patients. In particular, the possible relationship between SARS-CoV-2 mutant spectrum complexity and disease associations has not been established. In the present study, we report an ultra-deep sequencing (UDS) analysis of the mutant spectrum of amplicons from the nsp12 (polymerase)- and spike (S)-coding regions of thirty nasopharyngeal isolates (diagnostic samples) of SARS-CoV-2 of the first COVID-19 pandemic wave (Madrid, Spain, April 2020) classified according to the severity of ensuing COVID-19. Low frequency mutations and deletions, counted relative to the consensus sequence of the corresponding isolate, were overwhelmingly abundant. We show that the average number of different point mutations, mutations per haplotype and several diversity indices was significantly higher in SARS-CoV-2 isolated from patients who developed mild disease than in those associated with moderate or severe disease (exitus). No such bias was observed with RNA deletions. Location of amino acid substitutions in the three dimensional structures of nsp12 (polymerase) and S suggest significant structural or functional effects. Thus, patients who develop mild symptoms may be a richer source of genetic variants of SARS-CoV-2 than patients with moderate or severe COVID-19. IMPORTANCEThe study shows that mutant spectra of SARS-CoV-2 from diagnostic samples differ in point mutation abundance and complexity, and that significantly larger values were observed in virus from patients who developed mild COVID-19 symptoms. Mutant spectrum complexity is not a uniform trait among isolates. The nature and location of low frequency amino acid substitutions present in mutant spectra anticipate great potential for phenotypic diversification of SARS-CoV-2.

microbiology↗

bioRxiv2021

Chromosomal instability mediates immune exclusion and response to cytotoxic chemotherapy in colorectal liver metastases

The genomic drivers of immune exclusion in colorectal cancer liver metastases (CRCLM) remain poorly understood. Chromosomal instability (CIN), resulting in aneuploidy and genomic rearrangements, is the central pathway of mismatch repair-proficient colorectal cancer pathogenesis; however, it is unknown whether CIN impacts the outcomes of patients with limited spread of CRCLM treated with curative intent cytotoxic chemotherapy and surgery. Herein, we examined the relationship between CIN and the molecular subtypes of CRCLM, immune signaling, treatment sensitivity, and patient outcomes in three independent CRCLM patient cohorts. We established that a previously developed 70-gene CIN signature (CIN70) is a reliable measure of CIN, encompassing features of both aneuploidy and cellular proliferation. We demonstrated that tumors with the canonical subtype of CRCLM exhibit elevated levels of CIN and aneuploidy. Genomically unstable tumors were associated with an immune-depleted tumor microenvironment, and patients with genomically unstable tumors were at increased risk for disease progression in adverse metastatic sites, resulting in poor progression-free and overall survival. However, high-CIN tumors were particularly susceptible to DNA-damaging chemotherapies, including topoisomerase inhibitors, as well as radiation therapy. Treatment with genotoxic agents depleted CIN-rich cell populations, which resulted in a concomitant increase in intratumoral CD8+ T-cells in patients with primary rectal, breast, and bladder cancer. Taken together, we propose a mechanistic explanation for why cytotoxic chemotherapy can augment anti-tumor immunity and improve outcomes in patients with genomically unstable cancers.

cancer biology↗

bioRxiv2021

A two-level, dynamic fitness landscape of hepatitis C virus revealed by self-organized haplotype maps

Fitness landscapes reflect the adaptive potential of viruses. There is no information on how fitness peaks evolve when a virus replicates extensively in a controlled cell culture environment. Here we report the construction of Self-Organized Maps (SOMs), based on deep sequencing reads of three amplicons of the NS5A-NS5B-coding region of hepatitis C virus (HCV). A two-dimensional neural network was constructed and organized according to sequence relatedness. The third dimension of the fitness profile was given by the haplotype frequencies at each neuron. Fitness maps were derived for 44 HCV populations that share a common ancestor that was passaged up to 210 times in human hepatoma Huh-7.5 cells. As the virus increased its adaptation to the cells, the number of fitness peaks expanded, and their distribution shifted in sequence space. The landscape consisted of an extended basal platform, and a lower number of protruding higher fitness peaks. The function that relates fitness level and peak abundance corresponds a power law, a relationship observed with other complex natural phenomena. The dense basal platform may serve as spring-board to attain high fitness peaks. The study documents a highly dynamic, double-layer fitness landscape of HCV when evolving in a monotonous cell culture environment. This information may help interpreting HCV fitness landscapes in complex in vivo environments. IMPORTANCEThe study provides for the first time the fitness landscape of a virus in the course of its adaptation to a cell culture environment, in absence of external selective constraints. The deep sequencing-based self-organized maps document a two-layer fitness distribution with an ample basal platform, and a lower number of protruding, high fitness peaks. This landscape structure offers potential benefits for virus resilience to mutational inputs.

microbiology↗

bioRxiv2020

Amino acid substitutions associated with treatment failure of hepatitis C virus infection

Despite the high virological response rates achieved with current directly-acting antiviral agents (DAAs) against hepatitis C virus (HCV), around 2% to 5% of treated patients do not achieve a sustained viral response. Identification of amino acid substitutions associated with treatment failure requires analytical designs, such as subtype-specific ultra-deep sequencing (UDS) methods for HCV characterization and patient management. Using this procedure, we have identified six highly represented amino acid substitutions (HRSs) in NS5A and NS5B of HCV from 220 patients who failed therapy, which are not bona fide resistance-associated substitutions (RAS). They were present frequently in basal and post-treatment virus of patients who failed therapy to different DAA-based therapies. Contrary to several RAS, HRSs belong to the acceptable subset of substitutions according to the PAM250 replacement matrix. Coherently, their mutant frequency, measured by the number of deep sequencing reads within the HCV quasispecies that encode the relevant substitutions, ranged between 90% and 100% in most cases. Also, they have limited predicted disruptive effects on the three-dimensional structures of the proteins harboring them. Possible mechanisms of HRS origin and dominance, as well as their potential predictive value of treatment response are discussed.

microbiology↗

bioRxiv2020

Dissimilar conservation pattern in hepatitis C virus mutant spectra, consensus sequences, and data banks

The influence of quasispecies dynamics on long-term virus diversification in nature is a largely unexplored question. Specifically, whether intra-host nucleotide and amino acid variation in quasispecies fits variation observed in consensus sequences or data bank alignments is unknown. Genome conservation and dynamics simulations are used for the computational design of universal vaccines, therapeutic antibodies and pan-genomic antiviral agents. The expectation is that selection of escape mutants will be limited when mutations at conserved residues are required. This strategy assumes long-term (epidemiologically relevant) conservation but, critically, does not consider short-term (quasispecies-dictated) residue conservation. We have calculated mutant frequencies of individual loci from mutant spectra of hepatitis C virus (HCV) populations passaged in cell culture and from infected patients. Nucleotide or amino acid conservation in consensus sequences of the same populations, or in the Los Alamos HCV data bank did not match residue conservation in mutant spectra. The results relativize the concept of sequence conservation in viral genetics, and suggest that residue invariance in data banks is an insufficient basis for the design of universal viral ligands for clinical purposes. Our calculations suggest relaxed mutational restrictions during quasispecies dynamics, which may contribute to higher calculated short-term than long-term viral evolutionary rates.

microbiology↗

bioRxiv2020

Stabilising selection causes grossly altered but stable karyotypes in metastatic colorectal cancer

Aneuploidy, the loss and gain of whole and part chromosomes, is near-ubiquitous in cancer genomes and likely defines cancer cell biology. However, the temporal evolutionary dynamics that select for aneuploidy remain uncharacterised. Here we perform longitudinal genomic analysis of 755 samples from a total of 167 patients with colorectal-derived neoplastic lesions that represent distinct stages of tumour evolution through metastasis and treatment. Adenomas typically had few copy number alterations (CNAs) and most were subclonal, whereas cancers had many clonal CNAs, suggesting that progression goes through a CNA bottleneck. Individual CRC glands from the same tumour typically had very similar karyotypes, despite evidence of ongoing instability at the cell level in patient tumours, cell lines and organoids. CNAs in metastatic lesions sampled from liver and other organs, after chemotherapy or targeted therapies, and in late recurrences were typically similar to the primary tumour. Mathematical modelling and statistical inference indicated that these data are consistent with the action of negative selection on CNAs that traps cancer cell genomes on a fitness peak defined by the specific pattern of chromosomal aberrations. These data suggest that the initial progression of colorectal cancer requires the traversal of a rugged fitness landscape and subsequent CNA evolution, including metastatic dissemination and therapeutic resistance, is constrained by negative selection.

cancer biology↗

bioRxiv2020

Guanosine inhibits hepatitis C virus replication and increases indel frequencies, associated with altered intracellular nucleotide pools

In the course of experiments aimed at deciphering the inhibition mechanism of mycophenolic acid and ribavirin in hepatitis C virus (HCV) infection, we observed an inhibitory effect of the nucleoside guanosine (Gua). Here, we report that Gua and not the other standard nucleosides inhibits HCV replication in human hepatoma cells. Gua did not directly inhibit the in vitro polymerase activity of NS5B, but it modified the intracellular levels of nucleoside di- and tri-phosphate (NDPs and NTPs), leading to deficient HCV RNA replication and reduction of infectious progeny virus production. Changes in the concentrations of NTP or NDP modified NS5B RNA polymerase activity in vitro, in particular de novo RNA synthesis and template switching. Furthermore, the Gua-mediated changes were associated with a significant increase in the number of indels in viral RNA, which may account for the reduction of the specific infectivity of the viral progeny, suggesting the presence of defective genomes. Thus, a proper NTP:NDP balance appears to be critical to ensure HCV polymerase fidelity and minimal production of defective genomes. Author summaryRibonucleoside metabolism is essential for replication of RNA viruses. In this article we describe the antiviral activity of the natural ribonucleoside guanosine (Gua). We demonstrate that hepatitis C virus (HCV) replication is inhibited in the presence of increasing concentrations of this ribonucleoside and that this inhibition does not occur as a consequence of a direct inhibition of HCV polymerase. Cells exposed to increasing concentrations of Gua show imbalances in the intracellular concentrations of nucleoside-diphosphates and triphosphates and as the virus is passaged in these cells, it accumulates mutations that reduce its infectivity and decimate its normal spreading capacity.

microbiology↗

bioRxiv2019

Germline and somatic genetic variants in the p53 pathway interact to affect cancer risk, progression and drug response

Insights into oncogenesis derived from cancer susceptibility loci could facilitate better cancer management and treatment through precision oncology. However, therapeutic applications have thus far been limited by our current lack of understanding regarding both their interactions with somatic cancer driver mutations and their influence on tumorigenesis. Here, by integrating germline datasets relating to cancer susceptibility with tumour data capturing somatically-acquired genetic variation, we provide evidence that single nucleotide polymorphism (SNPs) and somatic mutations in the p53 tumor suppressor pathway can interact to influence cancer development, progression and treatment response. We go on to provide human genetic evidence of a tumor-promoting role for the pro-survival activities of p53, which supports the development of more effective therapy combinations through their inhibition in cancers retaining wild-type p53. SignificanceWe describe significant interactions between heritable and somatic genetic variants in the p53 pathway that affect cancer susceptibility, progression and treatment response. Our results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and identify novel therapeutic targets.

cancer biology↗

bioRxiv2019

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi- omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.

cancer biology↗

bioRxiv2019

Lethal mutagenesis of Rift Valley fever virus induced by favipiravir

Rift Valley fever virus (RVFV) is an emerging, mosquito-borne, zoonotic pathogen with recurrent outbreaks paying a considerable toll of human deaths in many African countries, for which no effective treatment is available. In cell culture studies and with laboratory animal models, the nucleoside analogue favipiravir (T-705) has demonstrated great potential for the treatment of several seasonal, chronic and emerging RNA virus infections of humans, suggesting applicability to control some viral outbreaks. Treatment with favipiravir was shown to reduce the infectivity of Rift Valley fever virus both in cell cultures and in experimental animal models, but the mechanism of this protective effect is not understood. In this work we show that favipiravir at concentrations well below the toxicity threshold estimated for cells is able to extinguish RVFV from infected cell cultures. Nucleotide sequence analysis has documented RVFV mutagenesis associated with virus extinction, with a significant increase in G to A and C to U transition frequencies, and a decrease of specific infectivity, hallmarks of lethal mutagenesis.

microbiology↗

bioRxiv2017

sigQC: A procedural approach for standardising the evaluation of gene signatures

With the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming necessary to determine whether a gene expression signature may apply to a dataset, but no standard quality control methodology exists. In this work, we introduce the first protocol, implemented in an R package sigQC, enabling a streamlined methodological and standardised approach for the quality control validation of gene signatures on independent data sets. The emphasis in this work is in showing the critical quality control steps involved in the generation of a clinically and biologically useful, transportable gene signature, including ensuring sufficient expression, variability, and autocorrelation of a signature. We demonstrate the application of the protocol in this work, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression data in cancer.

bioinformatics↗