Nanobodies targeting ABCC3 for immunotargeted applications in glioblastoma.

The cancer "omics" reveal many clinically relevant alterations that are transforming the molecular characterization of glioblastomas. However, many of these findings are not yet translated into clinical practice due, in part, to the lack of non-invasive biomarkers and the limitations imposed by the blood-brain barrier. Nanobodies, camelid single-domain antibody fragments, emerge as a promising tool for immunotargeted applications for diagnosing and treating glioblastomas. Performing agnostic bioinformatic analysis from glioblastoma patient datasets, we identified ATP Binding Cassette subfamily C member 3 (ABCC3) as a suitable target for immunotargeted applications. The expression of ABCC3 is associated with poor survival and impaired response to temozolomide. Importantly, high expression of ABCC3 is restricted to glioblastoma, with negligible levels in healthy brain tissue, and further correlates with tumor grade and stemness markers. We identified three immunogenic epitopes of ABCC3 which were used to isolate nanobodies from a glioblastoma-specific phage-display nanobody library. Two nanobodies targeting ABCC3 (NbA42 and NbA213) were further characterized and demonstrated in vivo selective recognition of ABCC3 in glioblastoma xenograft mouse models upon systemic administration. We designate NbA42 and NbA213 as new candidates to implement immunotargeted applications guiding a more personalized and precise diagnosis, monitoring, and treatment of glioblastoma patients.

Fatty acids homeostasis during fasting predicts protection from chemotherapy toxicity.

Fasting exerts beneficial effects in mice and humans, including protection from chemotherapy toxicity. To explore the involved mechanisms, we collect blood from humans and mice before and after 36 or 24 hours of fasting, respectively, and measure lipid composition of erythrocyte membranes, circulating micro RNAs (miRNAs), and RNA expression at peripheral blood mononuclear cells (PBMCs). Fasting coordinately affects the proportion of polyunsaturated versus saturated and monounsaturated fatty acids at the erythrocyte membrane; and reduces the expression of insulin signaling-related genes in PBMCs. When fasted for 24 hours before and 24 hours after administration of oxaliplatin or doxorubicin, mice show a strong protection from toxicity in several tissues. Erythrocyte membrane lipids and PBMC gene expression define two separate groups of individuals that accurately predict a differential protection from chemotherapy toxicity, with important clinical implications. Our results reveal a mechanism of fasting associated with lipid homeostasis, and provide biomarkers of fasting to predict fasting-mediated protection from chemotherapy toxicity.

Activation of the integrated stress response is a vulnerability for multidrug-resistant FBXW7-deficient cells.

FBXW7 is one of the most frequently mutated tumor suppressors, deficiency of which has been associated with resistance to some anticancer therapies. Through bioinformatics and genome-wide CRISPR screens, we here reveal that FBXW7 deficiency leads to multidrug resistance (MDR). Proteomic analyses found an upregulation of mitochondrial factors as a hallmark of FBXW7 deficiency, which has been previously linked to chemotherapy resistance. Despite this increased expression of mitochondrial factors, functional analyses revealed that mitochondria are under stress, and genetic or chemical targeting of mitochondria is preferentially toxic for FBXW7-deficient cells. Mechanistically, the toxicity of therapies targeting mitochondrial translation such as the antibiotic tigecycline relates to the activation of the integrated stress response (ISR) in a GCN2 kinase-dependent manner. Furthermore, the discovery of additional drugs that are toxic for FBXW7-deficient cells showed that all of them unexpectedly activate a GCN2-dependent ISR regardless of their accepted mechanism of action. Our study reveals that while one of the most frequent mutations in cancer reduces the sensitivity to the vast majority of available therapies, it renders cells vulnerable to ISR-activating drugs.

Midkine rewires the melanoma microenvironment toward a tolerogenic and immune-resistant state.

An open question in aggressive cancers such as melanoma is how malignant cells can shift the immune system to pro-tumorigenic functions. Here we identify midkine (MDK) as a melanoma-secreted driver of an inflamed, but immune evasive, microenvironment that defines poor patient prognosis and resistance to immune checkpoint blockade. Mechanistically, MDK was found to control the transcriptome of melanoma cells, allowing for coordinated activation of nuclear factor-κB and downregulation of interferon-associated pathways. The resulting MDK-modulated secretome educated macrophages towards tolerant phenotypes that promoted CD8+ T cell dysfunction. In contrast, genetic targeting of MDK sensitized melanoma cells to anti-PD-1/anti-PD-L1 treatment. Emphasizing the translational relevance of these findings, the expression profile of MDK-depleted tumors was enriched in key indicators of a good response to immune checkpoint blockers in independent patient cohorts. Together, these data reveal that MDK acts as an internal modulator of autocrine and paracrine signals that maintain immune suppression in aggressive melanomas.

Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships.

Comorbidity is a medical condition attracting increasing attention in healthcare and biomedical research. Little is known about the involvement of potential molecular factors leading to the emergence of a specific disease in patients affected by other conditions. We present here a disease interaction network inferred from similarities between patients' molecular profiles, which significantly recapitulates epidemiologically documented comorbidities. Furthermore, we identify disease patient-subgroups that present different molecular similarities with other diseases, some of them opposing the general tendencies observed at the disease level. Analyzing the generated patient-subgroup network, we identify genes involved in such relations, together with drugs whose effects are potentially associated with the observed comorbidities. All the obtained associations are available at the disease PERCEPTION portal (http://disease-perception.bsc.es).

vulcanSpot: a tool to prioritize therapeutic vulnerabilities in cancer.

MOTIVATION: Genetic alterations lead to tumor progression and cell survival but also uncover cancer-specific vulnerabilities on gene dependencies that can be therapeutically exploited. RESULTS: vulcanSpot is a novel computational approach implemented to expand the therapeutic options in cancer beyond known-driver genes unlocking alternative ways to target undruggable genes. The method integrates genome-wide information provided by massive screening experiments to detect genetic vulnerabilities associated to tumors. Then, vulcanSpot prioritizes drugs to target cancer-specific gene dependencies using a weighted scoring system based on well known drug-gene relationships and drug repositioning strategies. AVAILABILITY AND IMPLEMENTATION: http://www.vulcanspot.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer.

Background: Epidemiological and clinical evidence points to cancer as a comorbidity in people with autism spectrum disorders (ASD). A significant overlap of genes and biological processes between both diseases has also been reported. Methods: Here, for the first time, we compared the gene expression profiles of ASD frontal cortex tissues and 22 cancer types obtained by differential expression meta-analysis and report gene, pathway, and drug set-based overlaps between them. Results: Four cancer types (brain, thyroid, kidney, and pancreatic cancers) presented a significant overlap in gene expression deregulations in the same direction as ASD whereas two cancer types (lung and prostate cancers) showed differential expression profiles significantly deregulated in the opposite direction from ASD. Functional enrichment and LINCS L1000 based drug set enrichment analyses revealed the implication of several biological processes and pathways that were affected jointly in both diseases, including impairments of the immune system, and impairments in oxidative phosphorylation and ATP synthesis among others. Our data also suggest that brain and kidney cancer have patterns of transcriptomic dysregulation in the PI3K/AKT/MTOR axis that are similar to those found in ASD. Conclusions: Comparisons of ASD and cancer differential gene expression meta-analysis results suggest that brain, kidney, thyroid, and pancreatic cancers are candidates for direct comorbid associations with ASD. On the other hand, lung and prostate cancers are candidates for inverse comorbid associations with ASD. Joint perturbations in a set of specific biological processes underlie these associations which include several pathways previously implicated in both cancer and ASD encompassing immune system alterations, impairments of energy metabolism, cell cycle, and signaling through PI3K and G protein-coupled receptors among others. These findings could help to explain epidemiological observations pointing towards direct and inverse comorbid associations between ASD and specific cancer types and depict a complex scenario regarding the molecular patterns of association between ASD and cancer.

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data.

BACKGROUND: Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy. RESULTS: We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility. CONCLUSIONS: PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

Whole exome sequencing identifies PLEC, EXO5 and DNAH7 as novel susceptibility genes in testicular cancer.

Testicular germ cell tumors (TGCTs) are a clinically and pathologically heterogeneous disease, and little is known of its genetic basis. Only low susceptibility risk loci have been identified for both sporadic and familial cases. Therefore, we tried to identify new susceptibility genes responsible for familial testicular cancer that may contribute to increasing our knowledge about the genetic basis of the disease. Nineteen Spanish families with at least two affected individuals with TGCT were selected. WES was performed on those individuals using an Illumina Hiseq2000 sequencing platform. Data were analyzed under a monogenic and polygenic model of inheritance, and candidate variants were evaluated in a case-control association study performed on 391 Spanish sporadic cases and 1,170 healthy Spanish controls. Results were replicated in a second series consisting of 101 TGCTs from the Cancer Genome Atlas (TGCA) and 27,000 controls from the Exome Aggregation Consortium (ExAC) database. Logistic regression was carried out to analyze the association strength (risk) of candidate variants obtained among cases and controls in different populations. Despite the sample size, we detected a significant earlier age of onset in familial TGCT (28y) than sporadic cases (33y), using a Mann-Whitney U test. We identified significant variants in the comparative study of TGCT cases (391) versus controls (almost 1,170), and three of them [PLEC (OR = 6.28, p = 6.42 × 10-23 ) (p.Arg2016Trp), EXO5 (OR = 3.37, p = 4.82 × 10-09 ) (p.Arg344AlafsTer10) and DNAH7 (OR = 1.64, p = 0.048)] were replicated as potential candidates that may contribute to explaining the genetic basis of TGCT.

Pancreas Cancer Precision Treatment Using Avatar Mice from a Bioinformatics Perspective.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death among solid malignancies. Unfortunately, PDAC lethality has not substantially decreased over the past 20 years. This aggressiveness is related to the genomic complexity and heterogeneity of PDAC, but also to the absence of an effective screening for the detection of early-stage tumors and a lack of efficient therapeutic options. Therefore, there is an urgent need to improve the arsenal of anti-PDAC drugs for an effective treatment of these patients. Patient-derived xenograft (PDX) mouse models represent a promising strategy to personalize PDAC treatment, offering a bench testing of candidate treatments and helping to select empirical treatments in PDAC patients with no therapeutic targets. Moreover, bioinformatics-based approaches have the potential to offer systematic insights into PDAC etiology predicting putatively actionable tumor-specific genomic alterations, identifying novel biomarkers and generating disease-associated gene expression signatures. This review focuses on recent efforts to individualize PDAC treatments using PDX models. Additionally, we discuss the current understanding of the PDAC genomic landscape and the putative druggable targets derived from mutational studies. PDAC molecular subclassifications and gene expression profiling studies are reviewed as well. Finally, latest bioinformatics methodologies based on somatic variant detection and prioritization, in silico drug response prediction, and drug repositioning to improve the treatment of advanced PDAC tumors are also covered.

A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer's Disease, Glioblastoma and Lung cancer.

Epidemiological studies indicate that patients suffering from Alzheimer's disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the molecular scenarios that might underlie direct and inverse co-morbidities between these diseases. Transcriptomic meta-analyses reveal significant numbers of genes with inverse patterns of expression in Alzheimer's disease and lung cancer, and with similar patterns of expression in Alzheimer's disease and glioblastoma. These observations support the existence of molecular substrates that could at least partially account for these direct and inverse co-morbidity relationships. A functional analysis of the sets of deregulated genes points to the immune system, up-regulated in both Alzheimer's disease and glioblastoma, as a potential link between these two diseases. Mitochondrial metabolism is regulated oppositely in Alzheimer's disease and lung cancer, indicating that it may be involved in the inverse co-morbidity between these diseases. Finally, oxidative phosphorylation is a good candidate to play a dual role by decreasing or increasing the risk of lung cancer and glioblastoma in Alzheimer's disease.

Treatment of Pancreatic Cancer Patient-Derived Xenograft Panel with Metabolic Inhibitors Reveals Efficacy of Phenformin.

Purpose: To identify effective metabolic inhibitors to suppress the aggressive growth of pancreatic ductal adenocarcinoma (PDAC), we explored the in vivo antitumor efficacy of metabolic inhibitors, as single agents, in a panel of patient-derived PDAC xenograft models (PDX) and investigated whether genomic alterations of tumors correlate with the sensitivity to metabolic inhibitors.Experimental Design: Mice with established PDAC tumors from 6 to 13 individual PDXs were randomized and treated, once daily for 4 weeks, with either sterile PBS (vehicle) or the glutaminase inhibitor bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)ethyl sulfide (BPTES), transaminase inhibitor aminooxyacetate (AOA), pyruvate dehydrogenase kinase inhibitor dichloroacetate (DCA), autophagy inhibitor chloroquine (CQ), and mitochondrial complex I inhibitor phenformin/metformin.Results: Among the agents tested, phenformin showed significant tumor growth inhibition (>30% compared with vehicle) in 5 of 12 individual PDXs. Metformin, at a fivefold higher dose, displayed significant tumor growth inhibition in 3 of 12 PDXs similar to BPTES (2/8 PDXs) and DCA (2/6 PDXs). AOA and CQ had the lowest response rates. Gene set enrichment analysis conducted using the baseline gene expression profile of pancreatic tumors identified a gene expression signature that inversely correlated with phenformin sensitivity, which is in agreement with the phenformin gene expression signature of NIH Library of Integrated Network-based Cellular Signatures (LINCS). The PDXs that were more sensitive to phenformin showed a baseline reduction in amino acids and elevation in oxidized glutathione. There was no correlation between phenformin response and genetic alterations in KRAS, TP53, SMAD4, or PTENConclusions: Phenformin treatment showed relatively higher antitumor efficacy against established PDAC tumors, compared with the efficacy of other metabolic inhibitors and metformin. Phenformin treatment significantly diminished PDAC tumor progression and prolonged tumor doubling time. Overall, our results serve as a foundation for further evaluation of phenformin as a therapeutic agent in pancreatic cancer. Clin Cancer Res; 23(18); 5639-47. ©2017 AACR.

Targeted Sequencing Reveals Low-Frequency Variants in EPHA Genes as Markers of Paclitaxel-Induced Peripheral Neuropathy.

Purpose: Neuropathy is the dose-limiting toxicity of paclitaxel and a major cause for decreased quality of life. Genetic factors have been shown to contribute to paclitaxel neuropathy susceptibility; however, the major causes for interindividual differences remain unexplained. In this study, we identified genetic markers associated with paclitaxel-induced neuropathy through massive sequencing of candidate genes.Experimental Design: We sequenced the coding region of 4 EPHA genes, 5 genes involved in paclitaxel pharmacokinetics, and 30 Charcot-Marie-Tooth genes, in 228 cancer patients with no/low neuropathy or high-grade neuropathy during paclitaxel treatment. An independent validation series included 202 paclitaxel-treated patients. Variation-/gene-based analyses were used to compare variant frequencies among neuropathy groups, and Cox regression models were used to analyze neuropathy along treatment.Results: Gene-based analysis identified EPHA6 as the gene most significantly associated with paclitaxel-induced neuropathy. Low-frequency nonsynonymous variants in EPHA6 were present exclusively in patients with high neuropathy, and all affected the ligand-binding domain of the protein. Accumulated dose analysis in the discovery series showed a significantly higher neuropathy risk for EPHA5/6/8 low-frequency nonsynonymous variant carriers [HR, 14.60; 95% confidence interval (CI), 2.33-91.62; P = 0.0042], and an independent cohort confirmed an increased neuropathy risk (HR, 2.07; 95% CI, 1.14-3.77; P = 0.017). Combining the series gave an estimated 2.5-fold higher risk of neuropathy (95% CI, 1.46-4.31; P = 9.1 × 10-4).Conclusions: This first study sequencing EPHA genes revealed that low-frequency variants in EPHA6, EPHA5, and EPHA8 contribute to the susceptibility to paclitaxel-induced neuropathy. Furthermore, EPHA's neuronal injury repair function suggests that these genes might constitute important neuropathy markers for many neurotoxic drugs. Clin Cancer Res; 23(5); 1227-35. ©2016 AACR.

Core Circadian Clock Genes Regulate Leukemia Stem Cells in AML.

Leukemia stem cells (LSCs) have the capacity to self-renew and propagate disease upon serial transplantation in animal models, and elimination of this cell population is required for curative therapies. Here, we describe a series of pooled, in vivo RNAi screens to identify essential transcription factors (TFs) in a murine model of acute myeloid leukemia (AML) with genetically and phenotypically defined LSCs. These screens reveal the heterodimeric, circadian rhythm TFs Clock and Bmal1 as genes required for the growth of AML cells in vitro and in vivo. Disruption of canonical circadian pathway components produces anti-leukemic effects, including impaired proliferation, enhanced myeloid differentiation, and depletion of LSCs. We find that both normal and malignant hematopoietic cells harbor an intact clock with robust circadian oscillations, and genetic knockout models reveal a leukemia-specific dependence on the pathway. Our findings establish a role for the core circadian clock genes in AML.

Theories of Lethal Mutagenesis: From Error Catastrophe to Lethal Defection.

RNA viruses get extinct in a process called lethal mutagenesis when subjected to an increase in their mutation rate, for instance, by the action of mutagenic drugs. Several approaches have been proposed to understand this phenomenon. The extinction of RNA viruses by increased mutational pressure was inspired by the concept of the error threshold. The now classic quasispecies model predicts the existence of a limit to the mutation rate beyond which the genetic information of the wild type could not be efficiently transmitted to the next generation. This limit was called the error threshold, and for mutation rates larger than this threshold, the quasispecies was said to enter into error catastrophe. This transition has been assumed to foster the extinction of the whole population. Alternative explanations of lethal mutagenesis have been proposed recently. In the first place, a distinction is made between the error threshold and the extinction threshold, the mutation rate beyond which a population gets extinct. Extinction is explained from the effect the mutation rate has, throughout the mutational load, on the reproductive ability of the whole population. Secondly, lethal defection takes also into account the effect of interactions within mutant spectra, which have been shown to be determinant for the understanding the extinction of RNA virus due to an augmented mutational pressure. Nonetheless, some relevant issues concerning lethal mutagenesis are not completely understood yet, as so survival of the flattest, i.e. the development of resistance to lethal mutagenesis by evolving towards mutationally more robust regions of sequence space, or sublethal mutagenesis, i.e., the increase of the mutation rate below the extinction threshold which may boost the adaptability of RNA virus, increasing their ability to develop resistance to drugs (including mutagens). A better design of antiviral therapies will still require an improvement of our knowledge about lethal mutagenesis.

The advantage of arriving first: characteristic times in finite size populations of error-prone replicators.

We study the evolution of a finite size population formed by mutationally isolated lineages of error-prone replicators in a two-peak fitness landscape. Computer simulations are performed to gain a stochastic description of the system dynamics. More specifically, for different population sizes, we compute the probability of each lineage being selected in terms of their mutation rates and the amplification factors of the fittest phenotypes. We interpret the results as the compromise between the characteristic time a lineage takes to reach its fittest phenotype by crossing the neutral valley and the selective value of the sequences that form the lineages. A main conclusion is drawn: for finite population sizes, the survival probability of the lineage that arrives first to the fittest phenotype rises significantly.

Extinction of hepatitis C virus by ribavirin in hepatoma cells involves lethal mutagenesis.

Lethal mutagenesis, or virus extinction produced by enhanced mutation rates, is under investigation as an antiviral strategy that aims at counteracting the adaptive capacity of viral quasispecies, and avoiding selection of antiviral-escape mutants. To explore lethal mutagenesis of hepatitis C virus (HCV), it is important to establish whether ribavirin, the purine nucleoside analogue used in anti-HCV therapy, acts as a mutagenic agent during virus replication in cell culture. Here we report the effect of ribavirin during serial passages of HCV in human hepatoma Huh-7.5 cells, regarding viral progeny production and complexity of mutant spectra. Ribavirin produced an increase of mutant spectrum complexity and of the transition types associated with ribavirin mutagenesis, resulting in HCV extinction. Ribavirin-mediated depletion of intracellular GTP was not the major contributory factor to mutagenesis since mycophenolic acid evoked a similar decrease in GTP without an increase in mutant spectrum complexity. The intracellular concentration of the other nucleoside-triphosphates was elevated as a result of ribavirin treatment. Mycophenolic acid extinguished HCV without an intervening mutagenic activity. Ribavirin-mediated, but not mycophenolic acid-mediated, extinction of HCV occurred via a decrease of specific infectivity, a feature typical of lethal mutagenesis. We discuss some possibilities to explain disparate results on ribavirin mutagenesis of HCV.

Mutagenesis-mediated virus extinction: virus-dependent effect of viral load on sensitivity to lethal defection.

BACKGROUND: Lethal mutagenesis is a transition towards virus extinction mediated by enhanced mutation rates during viral genome replication, and it is currently under investigation as a potential new antiviral strategy. Viral load and virus fitness are known to influence virus extinction. Here we examine the effect or the multiplicity of infection (MOI) on progeny production of several RNA viruses under enhanced mutagenesis. RESULTS: The effect of the mutagenic base analogue 5-fluorouracil (FU) on the replication of the arenavirus lymphocytic choriomeningitis virus (LCMV) can result either in inhibition of progeny production and virus extinction in infections carried out at low multiplicity of infection (MOI), or in a moderate titer decrease without extinction at high MOI. The effect of the MOI is similar for LCMV and vesicular stomatitis virus (VSV), but minimal or absent for the picornaviruses foot-and-mouth disease virus (FMDV) and encephalomyocarditis virus (EMCV). The increase in mutation frequency and Shannon entropy (mutant spectrum complexity) as a result of virus passage in the presence of FU was more accentuated at low MOI for LCMV and VSV, and at high MOI for FMDV and EMCV. We present an extension of the lethal defection model that agrees with the experimental results. CONCLUSIONS: (i) Low infecting load favoured the extinction of negative strand viruses, LCMV or VSV, with an increase of mutant spectrum complexity. (ii) This behaviour is not observed in RNA positive strand viruses, FMDV or EMCV. (iii) The accumulation of defector genomes may underlie the MOI-dependent behaviour. (iv) LCMV coinfections are allowed but superinfection is strongly restricted in BHK-21 cells. (v) The dissimilar effects of the MOI on the efficiency of mutagenic-based extinction of different RNA viruses can have implications for the design of antiviral protocols based on lethal mutagenesis, presently under development.

Characteristic time in quasispecies evolution.

The time a phenotype takes to achieve a stationary state from an initial condition depends on multiple factors. In particular, it is a function of both its fitness and its mutation rate. We evaluate the average time, referred to as the characteristic time, T(c), that the system takes to reach a final steady state of simple models of populations formed by self-replicative sequences. The dependence of T(c) on the mutation rate and on the fitness landscape is also studied. For simple fitness landscapes, e.g. single peak, the characteristic time can be analytically obtained as a function of the system parameters. In this case, T(c) for obtaining the quasispecies distribution presents a maximum at a Q-value that depends on the initial conditions and decreases monotonously as the mutation rate tends to zero. For most of the complex landscapes handled in this paper, the characteristic time to achieve the quasispecies distribution picked around the fittest phenotype attains a local minimum for a given mutation rate between 0 and the Q-value at which T(c) reaches its local maximum. Thus, in these cases, an optimum value for the mutation rate exists that corresponds to the lowest value of the characteristic time for quasispecies evolution.

The relationship between the error catastrophe, survival of the flattest, and natural selection.

BACKGROUND: The quasispecies model is a general model of evolution that is generally applicable to replication up to high mutation rates. It predicts that at a sufficiently high mutation rate, quasispecies with higher mutational robustness can displace quasispecies with higher replicative capacity, a phenomenon called "survival of the flattest". In some fitness landscapes it also predicts the existence of a maximum mutation rate, called the error threshold, beyond which the quasispecies enters into error catastrophe, losing its genetic information. The aim of this paper is to study the relationship between survival of the flattest and the transition to error catastrophe, as well as the connection between these concepts and natural selection. RESULTS: By means of a very simplified model, we show that the transition to an error catastrophe corresponds to a value of zero for the selective coefficient of the mutant phenotype with respect to the master phenotype, indicating that transition to the error catastrophe is in this case similar to the selection of a more robust species. This correspondence has been confirmed by considering a single-peak landscape in which sequences are grouped with respect to their Hamming distant from the master sequence. When the robustness of a class is changed by modification of its quality factor, the distribution of the population changes in accordance with the new value of the robustness, although an error catastrophe can be detected at the same values as in the general case. When two quasispecies of different robustness competes with one another, the entry of one of them into error catastrophe causes displacement of the other, because of the greater robustness of the former. Previous works are explicitly reinterpreted in the light of the results obtained in this paper. CONCLUSIONS: The main conclusion of this paper is that the entry into error catastrophe is a specific case of survival of the flattest acting on phenotypes that differ in the trade-off between replicative ability and mutational robustness. In fact, entry into error catastrophe occurs when the mutant phenotype acquires a selective advantage over the master phenotype. As both entry into error catastrophe and survival of the flattest are caused by natural selection when mutation rate is increased, we propose differentiating between them by the level of selection at which natural selection acts. So we propose to consider the transition to error catastrophe as a phenomenon of intra-quasispecies selection, and survival of the flattest as a phenomenon of inter-quasispecies selection.