Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers.

Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy.

Protocol for comparing gene-level selection on coding mutations between two groups of samples with Coselens.

The study of genes that evolve under conditional selection can shed light on the genomic underpinnings of adaptation, revealing epistasis and phenotypic plasticity. This protocol describes how to use the Coselens package to compare gene-level selection between two groups of samples. After installing Coselens and preparing the datasets, a typical run on a laptop takes less than 10 min. Coselens is best suited to analyze somatic mutations and data from experimental evolution, for which independently evolved samples are available. For complete details on the use and execution of this protocol, please refer to Iranzo et al. (2022).1.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer.

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.

Determinism and Contingency Shape Metabolic Complementation in an Endosymbiotic Consortium.

Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism-which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca. Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid iBSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis (i.e., tryptophan and biotin biosynthesis) and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with iBSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions.

Colorectal Cancer: From the Genetic Model to Posttranscriptional Regulation by Noncoding RNAs.

Colorectal cancer is the third most common form of cancer in developed countries and, despite the improvements achieved in its treatment options, remains as one of the main causes of cancer-related death. In this review, we first focus on colorectal carcinogenesis and on the genetic and epigenetic alterations involved. In addition, noncoding RNAs have been shown to be important regulators of gene expression. We present a general overview of what is known about these molecules and their role and dysregulation in cancer, with a special focus on the biogenesis, characteristics, and function of microRNAs. These molecules are important regulators of carcinogenesis, progression, invasion, angiogenesis, and metastases in cancer, including colorectal cancer. For this reason, miRNAs can be used as potential biomarkers for diagnosis, prognosis, and efficacy of chemotherapeutic treatments, or even as therapeutic agents, or as targets by themselves. Thus, this review highlights the importance of miRNAs in the development, progression, diagnosis, and therapy of colorectal cancer and summarizes current therapeutic approaches for the treatment of colorectal cancer.

Nature lessons: The whitefly bacterial endosymbiont is a minimal amino acid factory with unusual energetics.

Reductive genome evolution is a universal phenomenon observed in endosymbiotic bacteria in insects. As the genome reduces its size and irreversibly losses coding genes, the functionalities of the cell system, including the energetics processes, are more restricted. Several energetic pathways can also be lost. How do these reduced metabolic networks sustain the energy needs of the system? Among the bacteria with reduced genomes Candidatus Portiera aleyrodidarum, obligate endosymbiont of whiteflies, represents an extreme case since lacks several key mechanisms for ATP generation. Thus, to analyze the cell energetics in this system, a genome-scale metabolic model of this endosymbiont was constructed, and its energy production capabilities dissected using stoichiometric analysis. Our results suggest that energy generation is coupled to the synthesis of essential amino acids and carotenoids, crucial metabolites in the symbiotic association. A deeper insight showed that ATP production via carotenoid synthesis is also connected with amino acid production. This unusual association of energy production with anabolism suggests that, although minimized, endosymbiont metabolic networks maintain a remarkable biosynthetic potential.

Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach.

Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.