Viktória Lázár

Bacterial evolution of antibiotic hypersensitivity

The evolution of resistance to a single antibiotic is frequently accompanied by increased resistance to multiple other antimicrobial agents. In sharp contrast, very little is known about the frequency and mechanisms underlying collateral sensitivity. In this case, genetic adaptation under antibiotic stress yields enhanced sensitivity to other antibiotics. Using large‐scale laboratory evolutionary experiments with Escherichia coli, we demonstrate that collateral sensitivity occurs frequently during the evolution of antibiotic resistance. Specifically, populations adapted to aminoglycosides have an especially low fitness in the presence of several other antibiotics. Whole‐genome sequencing of laboratory‐evolved strains revealed multiple mechanisms underlying aminoglycoside resistance, including a reduction in the proton‐motive force (PMF) across the inner membrane. We propose that as a side effect, these mutations diminish the activity of PMF‐dependent major efflux pumps (including the AcrAB transporter), leading to hypersensitivity to several other antibiotics. More generally, our work offers an insight into the mechanisms that drive the evolution of negative trade‐offs under antibiotic selection.

Network-level architecture and the evolutionary potential of underground metabolism

Significance Understanding how new metabolic pathways emerge is one of the key issues in evolutionary and systems biology. The prevailing paradigm is that evolution capitalizes on the weak side activities of preexisting enzymes (i.e. underground reactions). However, the extent to which underground reactions provide novelties in the context of the entire cellular system has remained unexplored. In this study, we present a comprehensive computational model of the underground metabolism of Escherichia coli. Together with a high-throughput experimental survey across hundreds of nutrient environments we predicted and confirmed new functional states of metabolism in which underground reactions allow growth when their activity is increased. Our approach has important implications for biotechnological and medical applications, such as understanding gain-of-function mutations in tumor development. A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

The Genomic Landscape of Compensatory Evolution

The Genomic Landscape of Compensatory Evolution Laboratory selection experiment explains how organisms compensate for the loss of genes during evolution, and reveals the deleterious side-effects of this process when adapting to novel environments.

Antagonism between Bacteriostatic and Bactericidal Antibiotics Is Prevalent

ABSTRACT Combination therapy is rarely used to counter the evolution of resistance in bacterial infections. Expansion of the use of combination therapy requires knowledge of how drugs interact at inhibitory concentrations. More than 50 years ago, it was noted that, if bactericidal drugs are most potent with actively dividing cells, then the inhibition of growth induced by a bacteriostatic drug should result in an overall reduction of efficacy when the drug is used in combination with a bactericidal drug. Our goal here was to investigate this hypothesis systematically. We first constructed time-kill curves using five different antibiotics at clinically relevant concentrations, and we observed antagonism between bactericidal and bacteriostatic drugs. We extended our investigation by performing a screen of pairwise combinations of 21 different antibiotics at subinhibitory concentrations, and we found that strong antagonistic interactions were enriched significantly among combinations of bacteriostatic and bactericidal drugs. Finally, since our hypothesis relies on phenotypic effects produced by different drug classes, we recreated these experiments in a microfluidic device and performed time-lapse microscopy to directly observe and quantify the growth and division of individual cells with controlled antibiotic concentrations. While our single-cell observations supported the antagonism between bacteriostatic and bactericidal drugs, they revealed an unexpected variety of cellular responses to antagonistic drug combinations, suggesting that multiple mechanisms underlie the interactions.

Collateral sensitivity of antibiotic-resistant microbes

Understanding how evolution of microbial resistance towards a given antibiotic influences susceptibility to other drugs is a challenge of profound importance. By combining laboratory evolution, genome sequencing, and functional analyses, recent works have charted the map of evolutionary trade-offs between antibiotics and have explored the underlying molecular mechanisms. Strikingly, mutations that caused multidrug resistance in bacteria simultaneously enhanced sensitivity to many other unrelated drugs (collateral sensitivity). Here, we explore how this emerging research sheds new light on resistance mechanisms and the way it could be exploited for the development of alternative antimicrobial strategies.

A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species

Significance Current tools for bacterial genome engineering suffer from major limitations. They have been optimized for a few laboratory model strains, lead to the accumulation of numerous undesired, off-target modifications, and demand extensive modification of the host genome prior to large-scale editing. Herein, we address these problems and present a simple, all-in-one solution. By utilizing a highly conserved mutant allele of the bacterial mismatch-repair system, we were able to gain unprecedented precision in the control over the generation of desired modifications in multiple bacterial species. These results have broad implications with regards to both biotechnological and clinical applications. Currently available tools for multiplex bacterial genome engineering are optimized for a few laboratory model strains, demand extensive prior modification of the host strain, and lead to the accumulation of numerous off-target modifications. Building on prior development of multiplex automated genome engineering (MAGE), our work addresses these problems in a single framework. Using a dominant-negative mutant protein of the methyl-directed mismatch repair (MMR) system, we achieved a transient suppression of DNA repair in Escherichia coli, which is necessary for efficient oligonucleotide integration. By integrating all necessary components into a broad-host vector, we developed a new workflow we term pORTMAGE. It allows efficient modification of multiple loci, without any observable off-target mutagenesis and prior modification of the host genome. Because of the conserved nature of the bacterial MMR system, pORTMAGE simultaneously allows genome editing and mutant library generation in other biotechnologically and clinically relevant bacterial species. Finally, we applied pORTMAGE to study a set of antibiotic resistance-conferring mutations in Salmonella enterica and E. coli. Despite over 100 million y of divergence between the two species, mutational effects remained generally conserved. In sum, a single transformation of a pORTMAGE plasmid allows bacterial species of interest to become an efficient host for genome engineering. These advances pave the way toward biotechnological and therapeutic applications. Finally, pORTMAGE allows systematic comparison of mutational effects and epistasis across a wide range of bacterial species.

Characterization of candidate gene copy number alterations in the 11q13 region along with BRAF and NRAS mutations in human melanoma

Amplification of the 11q13 chromosomal region is a common event in primary melanomas. Several candidate genes are localized at this sequence; however, their role in melanoma has not been clearly defined. The aim of this study was to develop an accurate method for determining the amplification pattern of six candidate genes that map to this amplicon core and to elucidate the possible relationship between BRAF, NRAS mutations and CCND1 copy number alterations, all of which are key components of the MAP kinase pathway. Characterization of gene copy numbers was performed by quantitative PCR and, as an alternative method, fluorescence in situ hybridization was used to define the CCND1 amplification pattern at the single cell level. Samples with amplified CCND1 (32%) were further analyzed for copy number alterations for the TAOS1, FGF3, FGF19, FGF4 and EMS1 genes. Coamplification of the CCND1 and TAOS1 was present in 15% of tumors and was more frequent in ulcerated lesions (P=0.017). Furthermore, 56% of primary melanomas had either BRAF or NRAS mutations, but these two mutations were not present in any of the lesions analyzed. Of these cases, 34% also had CCND1 amplification. There was a significant relationship between NRAS activating mutations and UV exposure (P=0.005). We did not find correlations between CCND1 gene amplification status and any of the patients’ clinicopathological parameters. However, CCND1 amplification simultaneously with either BRAF or NRAS activation mutations was observed mainly in primary tumors with ulcerated surfaces (P=0.028). We assume that coamplification of these candidate genes in the 11q13 region or CCND1 gene alterations along with either BRAF or NRAS mutations might be more important for prognosis than the presence of these alterations alone.

The role of CCND1 alterations during the progression of cutaneous malignant melanoma

It is well demonstrated that CCND1 amplification is a frequent event in the acral subtype of cutaneous malignant melanoma; however, its role in the other subtypes of the disease is still controversial. The objectives of this study were to evaluate genetic and expression alterations of CCND1 with a focus on primary cutaneous melanomas, to define BRAF and NRAS mutation status, and correlate the data with clinical–pathological parameters. CCND1 amplification was associated with ulceration and the localization of the metastasis. After correction for the mutation state of BRAF and NRAS genes, CCND1 amplification in samples without such mutations was associated with ulceration and sun exposure. The cyclin D1 (CCND1) mRNA level decreased in lesions with multiple metastases and was correlated with both the mRNA levels and mutation state of BRAF and NRAS genes. Primary melanomas with BRAFV600 or NRASQ61 mutations exhibited lower CCND1 mRNA level. CCND1 protein expression was associated with Breslow thickness, metastasis formation, and shorter survival time. These observations suggest that CCND1 alterations are linked to melanoma progression and are modified by BRAF and NRAS mutations. Our data show that CCND1 amplification could have a prognostic relevance in cutaneous melanoma and highlight that altered CCND1 gene expression may influence the metastatic progression, survival, and the localization of metastases.

Phenotypic heterogeneity promotes adaptive evolution

Genetically identical cells frequently display substantial heterogeneity in gene expression, cellular morphology and physiology. It has been suggested that by rapidly generating a subpopulation with novel phenotypic traits, phenotypic heterogeneity (or plasticity) accelerates the rate of adaptive evolution in populations facing extreme environmental challenges. This issue is important as cell-to-cell phenotypic heterogeneity may initiate key steps in microbial evolution of drug resistance and cancer progression. Here, we study how stochastic transitions between cellular states influence evolutionary adaptation to a stressful environment in yeast Saccharomyces cerevisiae. We developed inducible synthetic gene circuits that generate varying degrees of expression stochasticity of an antifungal resistance gene. We initiated laboratory evolutionary experiments with genotypes carrying different versions of the genetic circuit by exposing the corresponding populations to gradually increasing antifungal stress. Phenotypic heterogeneity altered the evolutionary dynamics by transforming the adaptive landscape that relates genotype to fitness. Specifically, it enhanced the adaptive value of beneficial mutations through synergism between cell-to-cell variability and genetic variation. Our work demonstrates that phenotypic heterogeneity is an evolving trait when populations face a chronic selection pressure. It shapes evolutionary trajectories at the genomic level and facilitates evolutionary rescue from a deteriorating environmental stress.

Adaptive evolution of complex innovations through stepwise metabolic niche expansion

A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.