Balázs Szappanos

An integrated approach to characterize genetic interaction networks in yeast metabolism

Although experimental and theoretical efforts have been applied to globally map genetic interactions, we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we i, quantitatively measured genetic interactions between ∼185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii, superposed the data on a detailed systems biology model of metabolism and iii, introduced a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigated the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability. Last, we show the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

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.

ModuLand plug-in for Cytoscape

UNLABELLED The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux or Mac OS. We demonstrate its use on protein structure and metabolic networks. AVAILABILITY The plug-in and its user guide can be downloaded freely from: http://www.linkgroup.hu/modules.php.

Genome-wide analysis captures the determinants of the antibiotic cross-resistance interaction network

Understanding how evolution of antimicrobial resistance increases resistance to other drugs is a challenge of profound importance. By combining experimental evolution and genome sequencing of 63 laboratory-evolved lines, we charted a map of cross-resistance interactions between antibiotics in Escherichia coli, and explored the driving evolutionary principles. Here, we show that (1) convergent molecular evolution is prevalent across antibiotic treatments, (2) resistance conferring mutations simultaneously enhance sensitivity to many other drugs and (3) 27% of the accumulated mutations generate proteins with compromised activities, suggesting that antibiotic adaptation can partly be achieved without gain of novel function. By using knowledge on antibiotic properties, we examined the determinants of cross-resistance and identified chemogenomic profile similarity between antibiotics as the strongest predictor. In contrast, cross-resistance between two antibiotics is independent of whether they show synergistic effects in combination. These results have important implications on the development of novel antimicrobial strategies.

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.

Indispensability of horizontally transferred genes and its impact on bacterial genome streamlining

Why are certain bacterial genomes so small and compact? The adaptive genome streamlining hypothesis posits that selection acts to reduce genome size because of the metabolic burden of replicating DNA. To reveal the impact of genome streamlining on cellular traits, we reduced the Escherichia coli genome by up to 20% by deleting regions which have been repeatedly subjects of horizontal transfer in nature. Unexpectedly, horizontally transferred genes not only confer utilization of specific nutrients and elevate tolerance to stresses, but also allow efficient usage of resources to build new cells, and hence influence fitness in routine and stressful environments alike. Genome reduction affected fitness not only by gene loss, but also by induction of a general stress response. Finally, we failed to find evidence that the advantage of smaller genomes would be due to a reduced metabolic burden of replicating DNA or a link with smaller cell size. We conclude that as the potential energetic benefit gained by deletion of short genomic segments is vanishingly small compared with the deleterious side effects of these deletions, selection for reduced DNA synthesis costs is unlikely to shape the evolution of small genomes.

The evolution of defense mechanisms correlate with the explosive diversification of autodigesting Coprinellus mushrooms (Agaricales, Fungi).

Bursts of diversification are known to have contributed significantly to the extant morphological and species diversity, but evidence for many of the theoretical predictions about adaptive radiations have remained contentious. Despite their tremendous diversity, patterns of evolutionary diversification and the contribution of explosive episodes in fungi are largely unknown. Here, using the genus Coprinellus (Psathyrellaceae, Agaricales) as a model, we report the first explosive fungal radiation and infer that the onset of the radiation correlates with a change from a multilayered to a much simpler defense structure on the fruiting bodies. We hypothesize that this change constitutes a key innovation, probably relaxing constraints on diversification imposed by nutritional investment into the development of protective tissues of fruiting bodies. Fossil calibration suggests that Coprinellus mushrooms radiated during the Miocene coinciding with global radiation of large grazing mammals following expansion of dry open grasslands. In addition to diversification rate-based methods, we test the hard polytomy hypothesis, by analyzing the resolvability of internal nodes of the backbone of the putative radiation using Reversible-Jump MCMC. We discuss potential applications and pitfalls of this approach as well as how biologically meaningful polytomies can be distinguished from alignment shortcomings. Our data provide insights into the nature of adaptive radiations in general by revealing a deceleration of morphological diversification through time. The dynamics of morphological diversification was approximated by obtaining the temporal distribution of state changes in discrete traits along the trees and comparing it with the tempo of lineage accumulation. We found that the number of state changes correlate with the number of lineages, even in parts of the tree with short internal branches, and peaks around the onset of the explosive radiation followed by a slowdown, most likely because of the decrease in available niches.

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.

Marked genetic differences between braf and nras mutated primary melanomas as revealed by array comparative genomic hybridization

Somatic mutations of BRAF and NRAS oncogenes are thought to be among the first steps in melanoma initiation, but these mutations alone are insufficient to cause tumor progression. Our group studied the distinct genomic imbalances of primary melanomas harboring different BRAF or NRAS genotypes. We also aimed to highlight regions of change commonly seen together in different melanoma subgroups. Array comparative genomic hybridization was performed to assess copy number changes in 47 primary melanomas. BRAF and NRAS were screened for mutations by melting curve analysis. Reverse transcription PCR and fluorescence in-situ hybridization were performed to confirm the array comparative genomic hybridization results. Pairwise comparisons revealed distinct genomic profiles between melanomas harboring different mutations. Primary melanomas with the BRAF mutation exhibited more frequent losses on 10q23–q26 and gains on chromosome 7 and 1q23–q25 compared with melanomas with the NRAS mutation. Loss on the 11q23–q25 sequence was found mainly in conjunction with the NRAS mutation. Primary melanomas without the BRAF or the NRAS mutation showed frequent alterations in chromosomes 17 and 4. Correlation analysis revealed chromosomal alterations that coexist more often in these tumor subgroups. To find classifiers for BRAF mutation, random forest analysis was used. Fifteen candidates emerged with 87% prediction accuracy. Signaling interactions between the EGF/MAPK–JAK pathways were observed to be extensively altered in melanomas with the BRAF mutation. We found marked differences in the genetic pattern of the BRAF and NRAS mutated melanoma subgroups that might suggest that these mutations contribute to malignant melanoma in conjunction with distinct cooperating oncogenic events.

Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal

Energy metabolism is central to cellular biology. Thus, genome-scale models of heterotrophic unicellular species must account appropriately for the utilization of external nutrients to synthesize energy metabolites such as ATP. However, metabolic models designed for flux-balance analysis (FBA) may contain thermodynamically impossible energy-generating cycles: without nutrient consumption, these models are still capable of charging energy metabolites (such as ADP→ATP or NADP+→NADPH). Here, we show that energy-generating cycles occur in over 85% of metabolic models without extensive manual curation, such as those contained in the ModelSEED and MetaNetX databases; in contrast, such cycles are rare in the manually curated models of the BiGG database. Energy generating cycles may represent model errors, e.g., erroneous assumptions on reaction reversibilities. Alternatively, part of the cycle may be thermodynamically feasible in one environment, while the remainder is thermodynamically feasible in another environment; as standard FBA does not account for thermodynamics, combining these into an FBA model allows erroneous energy generation. The presence of energy-generating cycles typically inflates maximal biomass production rates by 25%, and may lead to biases in evolutionary simulations. We present efficient computational methods (i) to identify energy generating cycles, using FBA, and (ii) to identify minimal sets of model changes that eliminate them, using a variant of the GlobalFit algorithm.